Python: fix(azure-ai): Fix response_format handling for structured outputs (#3114)

* fix(azure-ai): read response_format from chat_options instead of run_options

* refactor: use explicit None checks for response_format

* Fix mypy error

* Mypy fix

.github/actions/azure-functions-integration-setup/action.yml

@@ -12,7 +12,7 @@ runs:
             docker rm -f dts-emulator
           fi
           echo "Starting Durable Task Scheduler Emulator"
-          docker run -d --name dts-emulator -p 8080:8080 -p 8082:8082 -e DTS_USE_DYNAMIC_TASK_HUBS=true mcr.microsoft.com/dts/dts-emulator:latest
+          docker run -d --name dts-emulator -p 8080:8080 -p 8082:8082 mcr.microsoft.com/dts/dts-emulator:latest
           echo "Waiting for Durable Task Scheduler Emulator to be ready"
           timeout 30 bash -c 'until curl --silent http://localhost:8080/healthz; do sleep 1; done'
           echo "Durable Task Scheduler Emulator is ready"

.github/copilot-instructions.md

@@ -14,8 +14,6 @@ Here are some general guidelines that apply to all code.
 
 - The top of all *.cs files should have a copyright notice: `// Copyright (c) Microsoft. All rights reserved.`
 - All public methods and classes should have XML documentation comments.
-- After adding, modifying or deleting code, run `dotnet build`, and then fix any reported build errors.
-- After adding or modifying code, run `dotnet format` to automatically fix any formatting errors.
 
 ### C# Sample Code Guidelines
 

.github/upgrades/prompts/SemanticKernelToAgentFramework.md

@@ -105,7 +105,7 @@ After completing migration, verify these specific items:
 1. **Compilation**: Execute `dotnet build` on all modified projects - zero errors required
 2. **Namespace Updates**: Confirm all `using Microsoft.SemanticKernel.Agents` statements are replaced
 3. **Method Calls**: Verify all `InvokeAsync` calls are changed to `RunAsync`
-4. **Return Types**: Confirm handling of `AgentResponse` instead of `IAsyncEnumerable<AgentResponseItem<ChatMessageContent>>`
+4. **Return Types**: Confirm handling of `AgentRunResponse` instead of `IAsyncEnumerable<AgentResponseItem<ChatMessageContent>>`
 5. **Thread Creation**: Validate all thread creation uses `agent.GetNewThread()` pattern
 6. **Tool Registration**: Ensure `[KernelFunction]` attributes are removed and `AIFunctionFactory.Create()` is used
 7. **Options Configuration**: Verify `AgentRunOptions` or `ChatClientAgentRunOptions` replaces `AgentInvokeOptions`
@@ -119,7 +119,7 @@ Agent Framework provides functionality for creating and managing AI agents throu
 Key API differences:
 - Agent creation: Remove Kernel dependency, use direct client-based creation
 - Method names: `InvokeAsync` → `RunAsync`, `InvokeStreamingAsync` → `RunStreamingAsync`
-- Return types: `IAsyncEnumerable<AgentResponseItem<ChatMessageContent>>` → `AgentResponse`
+- Return types: `IAsyncEnumerable<AgentResponseItem<ChatMessageContent>>` → `AgentRunResponse`
 - Thread creation: Provider-specific constructors → `agent.GetNewThread()`
 - Tool registration: `KernelPlugin` system → Direct `AIFunction` registration
 - Options: `AgentInvokeOptions` → Provider-specific run options (e.g., `ChatClientAgentRunOptions`)
@@ -166,8 +166,8 @@ Replace these method calls:
 | `thread.DeleteAsync()` | Provider-specific cleanup | Use provider client directly |
 
 Return type changes:
-- `IAsyncEnumerable<AgentResponseItem<ChatMessageContent>>` → `AgentResponse`
-- `IAsyncEnumerable<StreamingChatMessageContent>` → `IAsyncEnumerable<AgentResponseUpdate>`
+- `IAsyncEnumerable<AgentResponseItem<ChatMessageContent>>` → `AgentRunResponse`
+- `IAsyncEnumerable<StreamingChatMessageContent>` → `IAsyncEnumerable<AgentRunResponseUpdate>`
 </api_changes>
 
 <configuration_changes>
@@ -191,8 +191,8 @@ Agent Framework changes these behaviors compared to Semantic Kernel Agents:
 1. **Thread Management**: Agent Framework automatically manages thread state. Semantic Kernel required manual thread updates in some scenarios (e.g., OpenAI Responses).
 
 2. **Return Types**:
-   - Non-streaming: Returns single `AgentResponse` instead of `IAsyncEnumerable<AgentResponseItem<ChatMessageContent>>`
-   - Streaming: Returns `IAsyncEnumerable<AgentResponseUpdate>` instead of `IAsyncEnumerable<StreamingChatMessageContent>`
+   - Non-streaming: Returns single `AgentRunResponse` instead of `IAsyncEnumerable<AgentResponseItem<ChatMessageContent>>`
+   - Streaming: Returns `IAsyncEnumerable<AgentRunResponseUpdate>` instead of `IAsyncEnumerable<StreamingChatMessageContent>`
 
 3. **Tool Registration**: Agent Framework uses direct function registration without requiring `[KernelFunction]` attributes.
 
@@ -397,7 +397,7 @@ await foreach (AgentResponseItem<ChatMessageContent> item in agent.InvokeAsync(u
 
 **With this Agent Framework non-streaming pattern:**
 ```csharp
-AgentResponse result = await agent.RunAsync(userInput, thread, options);
+AgentRunResponse result = await agent.RunAsync(userInput, thread, options);
 Console.WriteLine(result);
 ```
 
@@ -411,7 +411,7 @@ await foreach (StreamingChatMessageContent update in agent.InvokeStreamingAsync(
 
 **With this Agent Framework streaming pattern:**
 ```csharp
-await foreach (AgentResponseUpdate update in agent.RunStreamingAsync(userInput, thread, options))
+await foreach (AgentRunResponseUpdate update in agent.RunStreamingAsync(userInput, thread, options))
 {
     Console.Write(update);
 }
@@ -420,8 +420,8 @@ await foreach (AgentResponseUpdate update in agent.RunStreamingAsync(userInput,
 **Required changes:**
 1. Replace `agent.InvokeAsync()` with `agent.RunAsync()`
 2. Replace `agent.InvokeStreamingAsync()` with `agent.RunStreamingAsync()`
-3. Change return type handling from `IAsyncEnumerable<AgentResponseItem<ChatMessageContent>>` to `AgentResponse`
-4. Change streaming type from `StreamingChatMessageContent` to `AgentResponseUpdate`
+3. Change return type handling from `IAsyncEnumerable<AgentResponseItem<ChatMessageContent>>` to `AgentRunResponse`
+4. Change streaming type from `StreamingChatMessageContent` to `AgentRunResponseUpdate`
 5. Remove `await foreach` for non-streaming calls
 6. Access message content directly from result object instead of iterating
 </api_changes>
@@ -661,7 +661,7 @@ await foreach (var result in agent.InvokeAsync(input, thread, options))
 ```csharp
 ChatClientAgentRunOptions options = new(new ChatOptions { MaxOutputTokens = 1000 });
 
-AgentResponse result = await agent.RunAsync(input, thread, options);
+AgentRunResponse result = await agent.RunAsync(input, thread, options);
 Console.WriteLine(result);
 
 // Access underlying content when needed:
@@ -689,7 +689,7 @@ await foreach (var result in agent.InvokeAsync(input, thread, options))
 
 **With this Agent Framework non-streaming usage pattern:**
 ```csharp
-AgentResponse result = await agent.RunAsync(input, thread, options);
+AgentRunResponse result = await agent.RunAsync(input, thread, options);
 Console.WriteLine($"Tokens: {result.Usage.TotalTokenCount}");
 ```
 
@@ -709,7 +709,7 @@ await foreach (StreamingChatMessageContent response in agent.InvokeStreamingAsyn
 
 **With this Agent Framework streaming usage pattern:**
 ```csharp
-await foreach (AgentResponseUpdate update in agent.RunStreamingAsync(input, thread, options))
+await foreach (AgentRunResponseUpdate update in agent.RunStreamingAsync(input, thread, options))
 {
     if (update.Contents.OfType<UsageContent>().FirstOrDefault() is { } usageContent)
     {

.github/workflows/dotnet-build-and-test.yml

@@ -95,7 +95,7 @@ jobs:
           echo "COSMOS_EMULATOR_AVAILABLE=true" >> $env:GITHUB_ENV
 
       - name: Setup dotnet
-        uses: actions/setup-dotnet@v5.1.0
+        uses: actions/setup-dotnet@v5.0.1
         with:
           global-json-file: ${{ github.workspace }}/dotnet/global.json
       - name: Build dotnet solutions

.github/workflows/merge-gatekeeper.yml

@@ -29,4 +29,3 @@ jobs:
           token: ${{ secrets.GITHUB_TOKEN }}
           timeout: 3600
           interval: 30
-          ignored: CodeQL,CodeQL analysis (csharp)

.github/workflows/python-merge-tests.yml

@@ -97,7 +97,7 @@ jobs:
         id: azure-functions-setup
       - name: Test with pytest
         timeout-minutes: 10
-        run: uv run poe all-tests -n logical --dist loadfile --dist worksteal --timeout 600 --retries 3 --retry-delay 10
+        run: uv run poe all-tests -n logical --dist loadfile --dist worksteal --timeout 300 --retries 3 --retry-delay 10
         working-directory: ./python
       - name: Test core samples
         timeout-minutes: 10

.github/workflows/python-test-coverage-report.yml

@@ -34,16 +34,9 @@ jobs:
         # because the workflow_run event does not have access to the PR number
         # The PR number is needed to post the comment on the PR
         run: |
-          if [ ! -s pr_number ]; then
-            echo "PR number file 'pr_number' is missing or empty"
-            exit 1
-          fi
-          PR_NUMBER=$(head -1 pr_number | tr -dc '0-9')
-          if [ -z "$PR_NUMBER" ]; then
-            echo "PR number file 'pr_number' does not contain a valid PR number"
-            exit 1
-          fi
-          echo "PR_NUMBER=$PR_NUMBER" >> "$GITHUB_ENV"
+          PR_NUMBER=$(cat pr_number)
+          echo "PR number: $PR_NUMBER"
+          echo "PR_NUMBER=$PR_NUMBER" >> $GITHUB_ENV
       - name: Pytest coverage comment
         id: coverageComment
         uses: MishaKav/pytest-coverage-comment@v1.2.0

.gitignore

@@ -206,17 +206,15 @@ agents.md
 WARP.md
 **/memory-bank/
 **/projectBrief.md
-**/tmpclaude*
 
 # Azurite storage emulator files
-*/__azurite_db_blob__.json*
-*/__azurite_db_blob_extent__.json*
-*/__azurite_db_queue__.json*
-*/__azurite_db_queue_extent__.json*
-*/__azurite_db_table__.json*
+*/__azurite_db_blob__.json
+*/__azurite_db_blob_extent__.json
+*/__azurite_db_queue__.json
+*/__azurite_db_queue_extent__.json
+*/__azurite_db_table__.json
 */__blobstorage__/
 */__queuestorage__/
-*/AzuriteConfig
 
 # Azure Functions local settings
 local.settings.json
@@ -228,4 +226,3 @@ local.settings.json
 
 # Database files
 *.db
-python/dotnet-ref

docs/decisions/0001-agent-run-response.md

@@ -163,8 +163,8 @@ foreach (var update in response.Messages)
 ### Option 2 Run: Container with Primary and Secondary Properties, RunStreaming: Stream of Primary + Secondary
 
 Run returns a new response type that has separate properties for the Primary Content and the Secondary Updates leading up to it.
-The Primary content is available in the `AgentResponse.Messages` property while Secondary updates are in a new `AgentResponse.Updates` property.
-`AgentResponse.Text` returns the Primary content text.
+The Primary content is available in the `AgentRunResponse.Messages` property while Secondary updates are in a new `AgentRunResponse.Updates` property.
+`AgentRunResponse.Text` returns the Primary content text.
 
 Since streaming would still need to return an `IAsyncEnumerable` of updates, the design would differ from non-streaming.
 With non-streaming Primary and Secondary content is split into separate lists, while with streaming it's combined in one stream.
@@ -232,24 +232,24 @@ await foreach (var update in responses)
 ```csharp
 class Agent
 {
-    public abstract Task<AgentResponse> RunAsync(
+    public abstract Task<AgentRunResponse> RunAsync(
         IReadOnlyCollection<ChatMessage> messages,
         AgentThread? thread = null,
         AgentRunOptions? options = null,
         CancellationToken cancellationToken = default);
 
-    public abstract IAsyncEnumerable<AgentResponseUpdate> RunStreamingAsync(
+    public abstract IAsyncEnumerable<AgentRunResponseUpdate> RunStreamingAsync(
         IReadOnlyCollection<ChatMessage> messages,
         AgentThread? thread = null,
         AgentRunOptions? options = null,
         CancellationToken cancellationToken = default);
 }
 
-class AgentResponse : ChatResponse
+class AgentRunResponse : ChatResponse
 {
 }
 
-public class AgentResponseUpdate : ChatResponseUpdate
+public class AgentRunResponseUpdate : ChatResponseUpdate
 {
 }
 ```
@@ -265,20 +265,20 @@ The new types could also exclude properties that make less sense for agents, lik
 ```csharp
 class Agent
 {
-    public abstract Task<AgentResponse> RunAsync(
+    public abstract Task<AgentRunResponse> RunAsync(
         IReadOnlyCollection<ChatMessage> messages,
         AgentThread? thread = null,
         AgentRunOptions? options = null,
         CancellationToken cancellationToken = default);
 
-    public abstract IAsyncEnumerable<AgentResponseUpdate> RunStreamingAsync(
+    public abstract IAsyncEnumerable<AgentRunResponseUpdate> RunStreamingAsync(
         IReadOnlyCollection<ChatMessage> messages,
         AgentThread? thread = null,
         AgentRunOptions? options = null,
         CancellationToken cancellationToken = default);
 }
 
-class AgentResponse // Compare with ChatResponse
+class AgentRunResponse // Compare with ChatResponse
 {
     public string Text { get; } // Aggregation of TextContent from messages.
 
@@ -294,12 +294,12 @@ class AgentResponse // Compare with ChatResponse
     public AdditionalPropertiesDictionary? AdditionalProperties { get; set; }
 }
 
-// Not Included in AgentResponse compared to ChatResponse
+// Not Included in AgentRunResponse compared to ChatResponse
 public ChatFinishReason? FinishReason { get; set; }
 public string? ConversationId { get; set; }
 public string? ModelId { get; set; }
 
-public class AgentResponseUpdate // Compare with ChatResponseUpdate
+public class AgentRunResponseUpdate // Compare with ChatResponseUpdate
 {
     public string Text { get; } // Aggregation of TextContent from Contents.
 
@@ -317,7 +317,7 @@ public class AgentResponseUpdate // Compare with ChatResponseUpdate
     public AdditionalPropertiesDictionary? AdditionalProperties { get; set; }
 }
 
-// Not Included in AgentResponseUpdate compared to ChatResponseUpdate
+// Not Included in AgentRunResponseUpdate compared to ChatResponseUpdate
 public ChatFinishReason? FinishReason { get; set; }
 public string? ConversationId { get; set; }
 public string? ModelId { get; set; }
@@ -360,7 +360,7 @@ public class ChatFinishReason
 ### Option 2: Add another property on responses for AgentRun
 
 ```csharp
-class AgentResponse
+class AgentRunResponse
 {
     ...
     public AgentRun RunReference { get; set; } // Reference to long running process
@@ -368,7 +368,7 @@ class AgentResponse
 }
 
 
-public class AgentResponseUpdate
+public class AgentRunResponseUpdate
 {
     ...
     public AgentRun RunReference { get; set; } // Reference to long running process
@@ -424,7 +424,7 @@ Note that where an agent doesn't support structured output, it may also be possi
 See [Structured Outputs Support](#structured-outputs-support) for a comparison on what other agent frameworks and protocols support.
 
 To support a good user experience for structured outputs, I'm proposing that we follow the pattern used by MEAI.
-We would add a generic version of `AgentResponse<T>`, that allows us to get the agent result already deserialized into our preferred type.
+We would add a generic version of `AgentRunResponse<T>`, that allows us to get the agent result already deserialized into our preferred type.
 This would be coupled with generic overload extension methods for Run that automatically builds a schema from the supplied type and updates
 the run options.
 
@@ -438,14 +438,14 @@ class Movie
     public int ReleaseYear { get; set; }
 }
 
-AgentResponse<Movie[]> response = agent.RunAsync<Movie[]>("What are the top 3 children's movies of the 80s.");
+AgentRunResponse<Movie[]> response = agent.RunAsync<Movie[]>("What are the top 3 children's movies of the 80s.");
 Movie[] movies = response.Result
 ```
 
 If we only support requesting a schema at agent creation time or where an agent has a built in schema, the following would be the preferred approach:
 
 ```csharp
-AgentResponse response = agent.RunAsync("What are the top 3 children's movies of the 80s.");
+AgentRunResponse response = agent.RunAsync("What are the top 3 children's movies of the 80s.");
 Movie[] movies = response.TryParseStructuredOutput<Movie[]>();
 ```
 
@@ -463,7 +463,7 @@ Option 2 chosen so that we can vary Agent responses independently of Chat Client
 ### StructuredOutputs Decision
 
 We will not support structured output per run request, but individual agents are free to allow this on the concrete implementation or at construction time.
-We will however add support for easily extracting a structured output type from the `AgentResponse`.
+We will however add support for easily extracting a structured output type from the `AgentRunResponse`.
 
 ## Addendum 1: AIContext Derived Types for different response types / Gap Analysis (Work in progress)
 

docs/decisions/0005-python-naming-conventions.md

@@ -54,7 +54,7 @@ The table below represents the majority of the naming changes discussed in issue
 | *Mcp* & *Http* | *MCP* & *HTTP* | accepted | Acronyms should be uppercased in class names, according to PEP 8. | None |
 | `agent.run_streaming` | `agent.run_stream` | accepted | Shorter and more closely aligns with AutoGen and Semantic Kernel names for the same methods. | None |
 | `workflow.run_streaming` | `workflow.run_stream` | accepted | In sync with `agent.run_stream` and shorter and more closely aligns with AutoGen and Semantic Kernel names for the same methods. | None |
-| AgentResponse & AgentResponseUpdate | AgentResponse & AgentResponseUpdate | rejected | Rejected, because it is the response to a run invocation and AgentResponse is too generic. | None |
+| AgentRunResponse & AgentRunResponseUpdate | AgentResponse & AgentResponseUpdate | rejected | Rejected, because it is the response to a run invocation and AgentResponse is too generic. | None |
 | *Content | * | rejected | Rejected other content type renames (removing `Content` suffix) because it would reduce clarity and discoverability. | Item was also considered, but rejected as it is very similar to Content, but would be inconsistent with dotnet. |
 | ChatResponse & ChatResponseUpdate | Response & ResponseUpdate | rejected | Rejected, because Response is too generic. | None |
 

docs/decisions/0006-userapproval.md

@@ -161,11 +161,11 @@ while (response.ApprovalRequests.Count > 0)
     response = await agent.RunAsync(messages, thread);
 }
 
-class AgentResponse
+class AgentRunResponse
 {
     ...
 
-    // A new property on AgentResponse to aggregate the ApprovalRequestContent items from
+    // A new property on AgentRunResponse to aggregate the ApprovalRequestContent items from
     // the response messages (Similar to the Text property).
     public IEnumerable<ApprovalRequestContent> ApprovalRequests { get; set; }
 
@@ -251,11 +251,11 @@ while (response.UserInputRequests.Any())
     response = await agent.RunAsync(messages, thread);
 }
 
-class AgentResponse
+class AgentRunResponse
 {
     ...
 
-    // A new property on AgentResponse to aggregate the UserInputRequestContent items from
+    // A new property on AgentRunResponse to aggregate the UserInputRequestContent items from
     // the response messages (Similar to the Text property).
     public IReadOnlyList<UserInputRequestContent> UserInputRequests { get; set; }
 
@@ -366,11 +366,11 @@ while (response.UserInputRequests.Any())
     response = await agent.RunAsync(messages, thread);
 }
 
-class AgentResponse
+class AgentRunResponse
 {
     ...
 
-    // A new property on AgentResponse to aggregate the UserInputRequestContent items from
+    // A new property on AgentRunResponse to aggregate the UserInputRequestContent items from
     // the response messages (Similar to the Text property).
     public IEnumerable<UserInputRequestContent> UserInputRequests { get; set; }
 

docs/decisions/0007-agent-filtering-middleware.md

@@ -115,7 +115,7 @@ public class AIAgent
         }
     }
 
-    public async Task<AgentResponse> RunAsync(
+    public async Task<AgentRunResponse> RunAsync(
         IReadOnlyCollection<ChatMessage> messages,
         AgentThread? thread = null,
         AgentRunOptions? options = null,
@@ -135,7 +135,7 @@ public class AIAgent
         return context.Response ?? throw new InvalidOperationException("Agent execution did not produce a response");
     }
 
-    protected abstract Task<AgentResponse> ExecuteCoreLogicAsync(
+    protected abstract Task<AgentRunResponse> ExecuteCoreLogicAsync(
         IReadOnlyCollection<ChatMessage> messages,
         AgentThread? thread,
         AgentRunOptions? options,
@@ -190,7 +190,7 @@ internal sealed class GuardrailCallbackAgent : DelegatingAIAgent
 
     public GuardrailCallbackAgent(AIAgent innerAgent) : base(innerAgent) { }
 
-    public override async Task<AgentResponse> RunAsync(IEnumerable<ChatMessage> messages, AgentThread? thread = null, AgentRunOptions? options = null, CancellationToken cancellationToken = default)
+    public override async Task<AgentRunResponse> RunAsync(IEnumerable<ChatMessage> messages, AgentThread? thread = null, AgentRunOptions? options = null, CancellationToken cancellationToken = default)
     {
         var filteredMessages = this.FilterMessages(messages);
         Console.WriteLine($"Guardrail Middleware - Filtered messages: {new ChatResponse(filteredMessages).Text}");
@@ -202,14 +202,14 @@ internal sealed class GuardrailCallbackAgent : DelegatingAIAgent
         return response;
     }
 
-    public override async IAsyncEnumerable<AgentResponseUpdate> RunStreamingAsync(IEnumerable<ChatMessage> messages, AgentThread? thread = null, AgentRunOptions? options = null, [EnumeratorCancellation] CancellationToken cancellationToken = default)
+    public override async IAsyncEnumerable<AgentRunResponseUpdate> RunStreamingAsync(IEnumerable<ChatMessage> messages, AgentThread? thread = null, AgentRunOptions? options = null, [EnumeratorCancellation] CancellationToken cancellationToken = default)
     {
         var filteredMessages = this.FilterMessages(messages);
         await foreach (var update in this.InnerAgent.RunStreamingAsync(filteredMessages, thread, options, cancellationToken))
         {
             if (update.Text != null)
             {
-                yield return new AgentResponseUpdate(update.Role, this.FilterContent(update.Text));
+                yield return new AgentRunResponseUpdate(update.Role, this.FilterContent(update.Text));
             }
             else
             {
@@ -252,7 +252,7 @@ internal sealed class RunningCallbackHandlerAgent : DelegatingAIAgent
         this._func = func;
     }
 
-    public override async Task<AgentResponse> RunAsync(IEnumerable<ChatMessage> messages, AgentThread? thread = null, AgentRunOptions? options = null, CancellationToken cancellationToken = default)
+    public override async Task<AgentRunResponse> RunAsync(IEnumerable<ChatMessage> messages, AgentThread? thread = null, AgentRunOptions? options = null, CancellationToken cancellationToken = default)
     {
         var context = new AgentInvokeCallbackContext(this, messages, thread, options, isStreaming: false, cancellationToken);
 
@@ -469,7 +469,7 @@ public sealed class CallbackEnabledAgent : DelegatingAIAgent
         this._callbacksProcessor = callbackMiddlewareProcessor ?? new();
     }
 
-    public override async Task<AgentResponse> RunAsync(
+    public override async Task<AgentRunResponse> RunAsync(
         IEnumerable<ChatMessage> messages,
         AgentThread? thread = null,
         AgentRunOptions? options = null,
@@ -541,7 +541,7 @@ public abstract class AgentContext
 public class AgentRunContext : AgentContext
 {
     public IList<ChatMessage> Messages { get; set; }
-    public AgentResponse? Response { get; set; }
+    public AgentRunResponse? Response { get; set; }
     public AgentThread? Thread { get; }
 
     public AgentRunContext(AIAgent agent, IList<ChatMessage> messages, AgentThread? thread, AgentRunOptions? options)

docs/decisions/0009-support-long-running-operations.md

@@ -687,7 +687,7 @@ This section considers different options for exposing the `RunId`, `Status`, and
 #### 4.1. As AIContent
 
 The `AsyncRunContent` class will represent a long-running operation initiated and managed by an agent/LLM.
-Items of this content type will be returned in a chat message as part of the `AgentResponse` or `ChatResponse`
+Items of this content type will be returned in a chat message as part of the `AgentRunResponse` or `ChatResponse`
 response to represent the long-running operation.
 
 The `AsyncRunContent` class has two properties: `RunId` and `Status`. The `RunId` identifies the 
@@ -1162,29 +1162,29 @@ For cancellation and deletion of long-running operations, new methods will be ad
 public abstract class AIAgent
 {
     // Existing methods...
-    public Task<AgentResponse> RunAsync(string message, AgentThread? thread = null, AgentRunOptions? options = null, CancellationToken cancellationToken = default) { ... }
-    public IAsyncEnumerable<AgentResponseUpdate> RunStreamingAsync(string message, AgentThread? thread = null, AgentRunOptions? options = null, CancellationToken cancellationToken = default) { ... }
+    public Task<AgentRunResponse> RunAsync(string message, AgentThread? thread = null, AgentRunOptions? options = null, CancellationToken cancellationToken = default) { ... }
+    public IAsyncEnumerable<AgentRunResponseUpdate> RunStreamingAsync(string message, AgentThread? thread = null, AgentRunOptions? options = null, CancellationToken cancellationToken = default) { ... }
 
     // New methods for uncommon operations
-    public virtual Task<AgentResponse?> CancelRunAsync(string id, AgentCancelRunOptions? options = null, CancellationToken cancellationToken = default)
+    public virtual Task<AgentRunResponse?> CancelRunAsync(string id, AgentCancelRunOptions? options = null, CancellationToken cancellationToken = default)
     {
-        return Task.FromResult<AgentResponse?>(null);
+        return Task.FromResult<AgentRunResponse?>(null);
     }
 
-    public virtual Task<AgentResponse?> DeleteRunAsync(string id, AgentDeleteRunOptions? options = null, CancellationToken cancellationToken = default)
+    public virtual Task<AgentRunResponse?> DeleteRunAsync(string id, AgentDeleteRunOptions? options = null, CancellationToken cancellationToken = default)
     {
-        return Task.FromResult<AgentResponse?>(null);
+        return Task.FromResult<AgentRunResponse?>(null);
     }
 }
 
 // Agent that supports update and cancellation
 public class CustomAgent : AIAgent
 {
-    public override async Task<AgentResponse?> CancelRunAsync(string id, AgentCancelRunOptions? options = null, CancellationToken cancellationToken = default)
+    public override async Task<AgentRunResponse?> CancelRunAsync(string id, AgentCancelRunOptions? options = null, CancellationToken cancellationToken = default)
     {
         var response = await this._client.CancelRunAsync(id, options?.Thread?.ConversationId);
 
-        return ConvertToAgentResponse(response); 
+        return ConvertToAgentRunResponse(response); 
     }
 
     // No overload for DeleteRunAsync as it's not supported by the underlying API
@@ -1195,7 +1195,7 @@ AIAgent agent = new CustomAgent();
 
 AgentThread thread = agent.GetNewThread();
 
-AgentResponse response = await agent.RunAsync("What is the capital of France?");
+AgentRunResponse response = await agent.RunAsync("What is the capital of France?");
 
 response = await agent.CancelRunAsync(response.ResponseId, new AgentCancelRunOptions { Thread = thread });
 ```
@@ -1251,10 +1251,10 @@ public class AgentRunOptions
 AIAgent agent = ...; // Get an instance of an AIAgent
 
 // Start a long-running execution for the prompt if supported by the underlying API
-AgentResponse response = await agent.RunAsync("<prompt>", new AgentRunOptions { AllowLongRunningResponses = true });
+AgentRunResponse response = await agent.RunAsync("<prompt>", new AgentRunOptions { AllowLongRunningResponses = true });
 
 // Start a quick prompt
-AgentResponse response = await agent.RunAsync("<prompt>");
+AgentRunResponse response = await agent.RunAsync("<prompt>");
 ```
 
 **Pros:** 
@@ -1279,7 +1279,7 @@ Below are the details of the option selected for chat clients that is also selec
 #### 3.1 Continuation Token of a Custom Type
 
 This option suggests using `ContinuationToken` to encapsulate all properties representing a long-running operation. The continuation token will be returned by agents in the 
-`ContinuationToken` property of the `AgentResponse` and `AgentResponseUpdate` responses to indicate that the response is part of a long-running operation. A null value 
+`ContinuationToken` property of the `AgentRunResponse` and `AgentRunResponseUpdate` responses to indicate that the response is part of a long-running operation. A null value 
 of the property will indicate that the response is not part of a long-running operation or the long-running operation has been completed. Callers will set the token in the
 `ContinuationToken` property of the `AgentRunOptions` class in follow-up calls to the `Run{Streaming}Async` methods to indicate that they want to "continue" the long-running
 operation identified by the token.
@@ -1313,18 +1313,18 @@ public class AgentRunOptions
     public ResponseContinuationToken? ContinuationToken { get; set; }
 }
 
-public class AgentResponse
+public class AgentRunResponse
 {
     public ResponseContinuationToken? ContinuationToken { get; }
 }
  
-public class AgentResponseUpdate
+public class AgentRunResponseUpdate
 {
     public ResponseContinuationToken? ContinuationToken { get; }
 }
 
 // Usage example
-AgentResponse response = await agent.RunAsync("What is the capital of France?");
+AgentRunResponse response = await agent.RunAsync("What is the capital of France?");
 
 AgentRunOptions options = new() { ContinuationToken = response.ContinuationToken };
 

docs/decisions/0010-ag-ui-support.md

@@ -36,7 +36,7 @@ Chosen option: "Current approach with internal event types and framework-native
 
 - Protects consumers from protocol changes by keeping AG-UI events internal
 - Maintains framework abstractions through conversion at boundaries
-- Uses existing framework types (AgentResponseUpdate, ChatMessage) for public API
+- Uses existing framework types (AgentRunResponseUpdate, ChatMessage) for public API
 - Focuses on core text streaming functionality
 - Leverages existing properties (ConversationId, ResponseId, ErrorContent) instead of custom types
 - Provides bidirectional client and server support
@@ -69,7 +69,7 @@ Chosen option: "Current approach with internal event types and framework-native
 
 3. **Agent Factory Pattern** - `MapAGUIAgent` uses factory function `(messages) => AIAgent` to allow request-specific agent configuration supporting multi-tenancy
 
-4. **Bidirectional Conversion Architecture** - Symmetric conversion logic in shared namespace compiled into both packages for server (`AgentResponseUpdate` → AG-UI events) and client (AG-UI events → `AgentResponseUpdate`)
+4. **Bidirectional Conversion Architecture** - Symmetric conversion logic in shared namespace compiled into both packages for server (`AgentRunResponseUpdate` → AG-UI events) and client (AG-UI events → `AgentRunResponseUpdate`)
 
 5. **Thread Management** - `AGUIAgentThread` stores only `ThreadId` with thread ID communicated via `ConversationId`; applications manage persistence for parity with other implementations and to be compliant with the protocol. Future extensions will support having the server manage the conversation.
 

docs/decisions/0011-create-get-agent-api.md

@@ -1,368 +0,0 @@
----
-status: proposed
-contact: dmytrostruk
-date: 2025-12-12
-deciders: dmytrostruk, markwallace-microsoft, eavanvalkenburg, giles17
----
-
-# Create/Get Agent API
-
-## Context and Problem Statement
-
-There is a misalignment between the create/get agent API in the .NET and Python implementations.
-
-In .NET, the `CreateAIAgent` method can create either a local instance of an agent or a remote instance if the backend provider supports it. For remote agents, once the agent is created, you can retrieve an existing remote agent by using the `GetAIAgent` method. If a backend provider doesn't support remote agents, `CreateAIAgent` just initializes a new local agent instance and `GetAIAgent` is not available. There is also a `BuildAIAgent` method, which is an extension for the `ChatClientBuilder` class from `Microsoft.Extensions.AI`. It builds pipelines of `IChatClient` instances with an `IServiceProvider`. This functionality does not exist in Python, so `BuildAIAgent` is out of scope.
-
-In Python, there is only one `create_agent` method, which always creates a local instance of the agent. If the backend provider supports remote agents, the remote agent is created only on the first `agent.run()` invocation.
-
-Below is a short summary of different providers and their APIs in .NET:
-
-| Package | Method | Behavior | Python support |
-|---|---|---|---|
-| Microsoft.Agents.AI | `CreateAIAgent` (based on `IChatClient`) | Creates a local instance of `ChatClientAgent`. | Yes (`create_agent` in `BaseChatClient`). |
-| Microsoft.Agents.AI.Anthropic | `CreateAIAgent` (based on `IBetaService` and `IAnthropicClient`) | Creates a local instance of `ChatClientAgent`. | Yes (`AnthropicClient` inherits `BaseChatClient`, which exposes `create_agent`). |
-| Microsoft.Agents.AI.AzureAI (V2) | `GetAIAgent` (based on `AIProjectClient` with `AgentReference`) | Creates a local instance of `ChatClientAgent`. | Partial (Python uses `create_agent` from `BaseChatClient`). |
-| Microsoft.Agents.AI.AzureAI (V2) | `GetAIAgent`/`GetAIAgentAsync` (with `Name`/`ChatClientAgentOptions`) | Fetches `AgentRecord` via HTTP, then creates a local `ChatClientAgent` instance. | No |
-| Microsoft.Agents.AI.AzureAI (V2) | `CreateAIAgent`/`CreateAIAgentAsync` (based on `AIProjectClient`) | Creates a remote agent first, then wraps it into a local `ChatClientAgent` instance. | No |
-| Microsoft.Agents.AI.AzureAI.Persistent (V1) | `GetAIAgent` (based on `PersistentAgentsClient` with `PersistentAgent`) | Creates a local instance of `ChatClientAgent`. | Partial (Python uses `create_agent` from `BaseChatClient`). |
-| Microsoft.Agents.AI.AzureAI.Persistent (V1) | `GetAIAgent`/`GetAIAgentAsync` (with `AgentId`) | Fetches `PersistentAgent` via HTTP, then creates a local `ChatClientAgent` instance. | No |
-| Microsoft.Agents.AI.AzureAI.Persistent (V1) | `CreateAIAgent`/`CreateAIAgentAsync` | Creates a remote agent first, then wraps it into a local `ChatClientAgent` instance. | No |
-| Microsoft.Agents.AI.OpenAI | `GetAIAgent` (based on `AssistantClient` with `Assistant`) | Creates a local instance of `ChatClientAgent`. | Partial (Python uses `create_agent` from `BaseChatClient`). |
-| Microsoft.Agents.AI.OpenAI | `GetAIAgent`/`GetAIAgentAsync` (with `AgentId`) | Fetches `Assistant` via HTTP, then creates a local `ChatClientAgent` instance. | No |
-| Microsoft.Agents.AI.OpenAI | `CreateAIAgent`/`CreateAIAgentAsync` (based on `AssistantClient`) | Creates a remote agent first, then wraps it into a local `ChatClientAgent` instance. | No |
-| Microsoft.Agents.AI.OpenAI | `CreateAIAgent` (based on `ChatClient`) | Creates a local instance of `ChatClientAgent`. | Yes (`create_agent` in `BaseChatClient`). |
-| Microsoft.Agents.AI.OpenAI | `CreateAIAgent` (based on `OpenAIResponseClient`) | Creates a local instance of `ChatClientAgent`. | Yes (`create_agent` in `BaseChatClient`). |
-
-Another difference between Python and .NET implementation is that in .NET `CreateAIAgent`/`GetAIAgent` methods are implemented as extension methods based on underlying SDK client, like `AIProjectClient` from Azure AI or `AssistantClient` from OpenAI:
-
-```csharp
-// Definition
-public static ChatClientAgent CreateAIAgent(
-    this AIProjectClient aiProjectClient,
-    string name,
-    string model,
-    string instructions,
-    string? description = null,
-    IList<AITool>? tools = null,
-    Func<IChatClient, IChatClient>? clientFactory = null,
-    IServiceProvider? services = null,
-    CancellationToken cancellationToken = default)
-{ }
-
-// Usage
-AIProjectClient aiProjectClient = new(new Uri(endpoint), new AzureCliCredential()); // Initialization of underlying SDK client
-
-var newAgent = await aiProjectClient.CreateAIAgentAsync(name: AgentName, model: deploymentName, instructions: AgentInstructions, tools: [tool]); // ChatClientAgent creation from underlying SDK client
-
-// Alternative usage (same as extension method, just explicit syntax)
-var newAgent = await AzureAIProjectChatClientExtensions.CreateAIAgentAsync(
-    aiProjectClient,
-    name: AgentName,
-    model: deploymentName,
-    instructions: AgentInstructions,
-    tools: [tool]);
-```
-
-Python doesn't support extension methods. Currently `create_agent` method is defined on `BaseChatClient`, but this method only creates a local instance of `ChatAgent` and it can't create remote agents for providers that support it for a couple of reasons:
-
-- It's defined as non-async.
-- `BaseChatClient` implementation is stateful for providers like Azure AI or OpenAI Assistants. The implementation stores agent/assistant metadata like `AgentId` and `AgentName`, so currently it's not possible to create different instances of `ChatAgent` from a single `BaseChatClient` in case if the implementation is stateful.
-
-## Decision Drivers
-
-- API should be aligned between .NET and Python.
-- API should be intuitive and consistent between backend providers in .NET and Python.
-
-## Considered Options
-
-Add missing implementations on the Python side. This should include the following:
-
-### agent-framework-azure-ai (both V1 and V2)
-
-- Add a `get_agent` method that accepts an underlying SDK agent instance and creates a local instance of `ChatAgent`.
-- Add a `get_agent` method that accepts an agent identifier, performs an additional HTTP request to fetch agent data, and then creates a local instance of `ChatAgent`.
-- Override the `create_agent` method from `BaseChatClient` to create a remote agent instance and wrap it into a local `ChatAgent`.
-
-.NET:
-
-```csharp
-var agent1 = new AIProjectClient(...).GetAIAgent(agentInstanceFromSdkType); // Creates a local ChatClientAgent instance from Azure.AI.Projects.OpenAI.AgentReference 
-var agent2 = new AIProjectClient(...).GetAIAgent(agentName); // Fetches agent data, creates a local ChatClientAgent instance
-var agent3 = new AIProjectClient(...).CreateAIAgent(...); // Creates a remote agent, returns a local ChatClientAgent instance
-```
-
-### agent-framework-core (OpenAI Assistants)
-
-- Add a `get_agent` method that accepts an underlying SDK agent instance and creates a local instance of `ChatAgent`.
-- Add a `get_agent` method that accepts an agent name, performs an additional HTTP request to fetch agent data, and then creates a local instance of `ChatAgent`.
-- Override the `create_agent` method from `BaseChatClient` to create a remote agent instance and wrap it into a local `ChatAgent`.
-
-.NET:
-
-```csharp
-var agent1 = new AssistantClient(...).GetAIAgent(agentInstanceFromSdkType); // Creates a local ChatClientAgent instance from OpenAI.Assistants.Assistant
-var agent2 = new AssistantClient(...).GetAIAgent(agentId); // Fetches agent data, creates a local ChatClientAgent instance
-var agent3 = new AssistantClient(...).CreateAIAgent(...); // Creates a remote agent, returns a local ChatClientAgent instance
-```
-
-### Possible Python implementations
-
-Methods like `create_agent` and `get_agent` should be implemented separately or defined on some stateless component that will allow to create multiple agents from the same instance/place.
-
-Possible options:
-
-#### Option 1: Module-level functions
-
-Implement free functions in the provider package that accept the underlying SDK client as the first argument (similar to .NET extension methods, but expressed in Python).
-
-Example:
-
-```python
-from agent_framework.azure import create_agent, get_agent
-
-ai_project_client = AIProjectClient(...)
-
-# Creates a remote agent first, then returns a local ChatAgent wrapper
-created_agent = await create_agent(
-    ai_project_client,
-    name="",
-    instructions="",
-    tools=[tool],
-)
-
-# Gets an existing remote agent and returns a local ChatAgent wrapper
-first_agent = await get_agent(ai_project_client, agent_id=agent_id)
-
-# Wraps an SDK agent instance (no extra HTTP call)
-second_agent = get_agent(ai_project_client, agent_reference)
-```
-
-Pros:
-
-- Naturally supports async `create_agent` / `get_agent`.
-- Supports multiple agents per SDK client.
-- Closest conceptual match to .NET extension methods while staying Pythonic.
-
-Cons:
-
-- Discoverability is lower (users need to know where the functions live).
-- Verbose when creating multiple agents (client must be passed every time):
-
-  ```python
-  agent1 = await azure_agents.create_agent(client, name="Agent1", ...)
-  agent2 = await azure_agents.create_agent(client, name="Agent2", ...)
-  ```
-
-#### Option 2: Provider object
-
-Introduce a dedicated provider type that is constructed from the underlying SDK client, and exposes async `create_agent` / `get_agent` methods.
-
-Example:
-
-```python
-from agent_framework.azure import AzureAIAgentProvider
-
-ai_project_client = AIProjectClient(...)
-provider = AzureAIAgentProvider(ai_project_client)
-
-agent = await provider.create_agent(
-    name="",
-    instructions="",
-    tools=[tool],
-)
-
-agent = await provider.get_agent(agent_id=agent_id)
-agent = provider.get_agent(agent_reference=agent_reference)
-```
-
-Pros:
-
-- High discoverability and clear grouping of related behavior.
-- Keeps SDK clients unchanged and supports multiple agents per SDK client.
-- Concise when creating multiple agents (client passed once):
-
-  ```python
-  provider = AzureAIAgentProvider(ai_project_client)
-  agent1 = await provider.create_agent(name="Agent1", ...)
-  agent2 = await provider.create_agent(name="Agent2", ...)
-  ```
-
-Cons:
-
-- Adds a new public concept/type for users to learn.
-
-#### Option 3: Inheritance (SDK client subclass)
-
-Create a subclass of the underlying SDK client and add `create_agent` / `get_agent` methods.
-
-Example:
-
-```python
-class ExtendedAIProjectClient(AIProjectClient):
-    async def create_agent(self, *, name: str, model: str, instructions: str, **kwargs) -> ChatAgent:
-        ...
-
-    async def get_agent(self, *, agent_id: str | None = None, sdk_agent=None, **kwargs) -> ChatAgent:
-        ...
-
-client = ExtendedAIProjectClient(...)
-agent = await client.create_agent(name="", instructions="")
-```
-
-Pros:
-
-- Discoverable and ergonomic call sites.
-- Mirrors the .NET “methods on the client” feeling.
-
-Cons:
-
-- Many SDK clients are not designed for inheritance; SDK upgrades can break subclasses.
-- Users must opt into subclass everywhere.
-- Typing/initialization can be tricky if the SDK client has non-trivial constructors.
-
-#### Option 4: Monkey patching
-
-Attach `create_agent` / `get_agent` methods to an SDK client class (or instance) at runtime.
-
-Example:
-
-```python
-def _create_agent(self, *, name: str, model: str, instructions: str, **kwargs) -> ChatAgent:
-    ...
-
-AIProjectClient.create_agent = _create_agent  # monkey patch
-```
-
-Pros:
-
-- Produces “extension method-like” call sites without wrappers or subclasses.
-
-Cons:
-
-- Fragile across SDK updates and difficult to type-check.
-- Surprising behavior (global side effects), potential conflicts across packages.
-- Harder to support/debug, especially in larger apps and test suites.
-
-## Decision Outcome
-
-Implement `create_agent`/`get_agent`/`as_agent` API via **Option 2: Provider object**.
-
-### Rationale
-
-| Aspect | Option 1 (Functions) | Option 2 (Provider) |
-|--------|----------------------|---------------------|
-| Multiple implementations | One package may contain V1, V2, and other agent types. Function names like `create_agent` become ambiguous - which agent type does it create? | Each provider class is explicit: `AzureAIAgentsProvider` vs `AzureAIProjectAgentProvider` |
-| Discoverability | Users must know to import specific functions from the package | IDE autocomplete on provider instance shows all available methods |
-| Client reuse | SDK client must be passed to every function call: `create_agent(client, ...)`, `get_agent(client, ...)` | SDK client passed once at construction: `provider = Provider(client)` |
-
-**Option 1 example:**
-```python
-from agent_framework.azure import create_agent, get_agent
-agent1 = await create_agent(client, name="Agent1", ...)  # Which agent type, V1 or V2?
-agent2 = await create_agent(client, name="Agent2", ...)  # Repetitive client passing
-```
-
-**Option 2 example:**
-```python
-from agent_framework.azure import AzureAIProjectAgentProvider
-provider = AzureAIProjectAgentProvider(client)  # Clear which service, client passed once
-agent1 = await provider.create_agent(name="Agent1", ...)
-agent2 = await provider.create_agent(name="Agent2", ...)
-```
-
-### Method Naming
-
-| Operation | Python | .NET | Async |
-|-----------|--------|------|-------|
-| Create on service | `create_agent()` | `CreateAIAgent()` | Yes |
-| Get from service | `get_agent(id=...)` | `GetAIAgent(agentId)` | Yes |
-| Wrap SDK object | `as_agent(reference)` | `AsAIAgent(agentInstance)` | No |
-
-The method names (`create_agent`, `get_agent`) do not explicitly mention "service" or "remote" because:
-- In Python, the provider class name explicitly identifies the service (`AzureAIAgentsProvider`, `OpenAIAssistantProvider`), making additional qualifiers in method names redundant.
-- In .NET, these are extension methods on `AIProjectClient` or `AssistantClient`, which already imply service operations.
-
-### Provider Class Naming
-
-| Package | Provider Class | SDK Client | Service |
-|---------|---------------|------------|---------|
-| `agent_framework.azure` | `AzureAIProjectAgentProvider` | `AIProjectClient` | Azure AI Agent Service, based on Responses API (V2) |
-| `agent_framework.azure` | `AzureAIAgentsProvider` | `AgentsClient` | Azure AI Agent Service (V1) |
-| `agent_framework.openai` | `OpenAIAssistantProvider` | `AsyncOpenAI` | OpenAI Assistants API |
-
-> **Note:** Azure AI naming is temporary. Final naming will be updated according to Azure AI / Microsoft Foundry renaming decisions.
-
-### Usage Examples
-
-#### Azure AI Agent Service V2 (based on Responses API)
-
-```python
-from agent_framework.azure import AzureAIProjectAgentProvider
-from azure.ai.projects import AIProjectClient
-
-client = AIProjectClient(endpoint, credential)
-provider = AzureAIProjectAgentProvider(client)
-
-# Create new agent on service
-agent = await provider.create_agent(name="MyAgent", model="gpt-4", instructions="...")
-
-# Get existing agent by name
-agent = await provider.get_agent(agent_name="MyAgent")
-
-# Wrap already-fetched SDK object (no HTTP calls)
-agent_ref = await client.agents.get("MyAgent")
-agent = provider.as_agent(agent_ref)
-```
-
-#### Azure AI Persistent Agents V1
-
-```python
-from agent_framework.azure import AzureAIAgentsProvider
-from azure.ai.agents import AgentsClient
-
-client = AgentsClient(endpoint, credential)
-provider = AzureAIAgentsProvider(client)
-
-agent = await provider.create_agent(name="MyAgent", model="gpt-4", instructions="...")
-agent = await provider.get_agent(agent_id="persistent-agent-456")
-agent = provider.as_agent(persistent_agent)
-```
-
-#### OpenAI Assistants
-
-```python
-from agent_framework.openai import OpenAIAssistantProvider
-from openai import OpenAI
-
-client = OpenAI()
-provider = OpenAIAssistantProvider(client)
-
-agent = await provider.create_agent(name="MyAssistant", model="gpt-4", instructions="...")
-agent = await provider.get_agent(assistant_id="asst_123")
-agent = provider.as_agent(assistant)
-```
-
-#### Local-Only Agents (No Provider)
-
-Current method `create_agent` (python) / `CreateAIAgent` (.NET) can be renamed to `as_agent` (python) / `AsAIAgent` (.NET) to emphasize the conversion logic rather than creation/initialization logic and to avoid collision with `create_agent` method for remote calls.
-
-```python
-from agent_framework import ChatAgent
-from agent_framework.openai import OpenAIChatClient
-
-# Convert chat client to ChatAgent (no remote service involved)
-client = OpenAIChatClient(model="gpt-4")
-agent = client.as_agent(name="LocalAgent", instructions="...") # instead of create_agent
-```
-
-### Adding New Agent Types
-
-Python:
-
-1. Create provider class in appropriate package.
-2. Implement `create_agent`, `get_agent`, `as_agent` as applicable.
-
-.NET:
-
-1. Create static class for extension methods.
-2. Implement `CreateAIAgentAsync`, `GetAIAgentAsync`, `AsAIAgent` as applicable.

docs/decisions/0012-python-typeddict-options.md

@@ -1,129 +0,0 @@
----
-# These are optional elements. Feel free to remove any of them.
-status: proposed
-contact: eavanvalkenburg
-date: 2026-01-08
-deciders: eavanvalkenburg, markwallace-microsoft,  sphenry, alliscode, johanst, brettcannon
-consulted: taochenosu, moonbox3, dmytrostruk, giles17
----
-
-# Leveraging TypedDict and Generic Options in Python Chat Clients
-
-## Context and Problem Statement
-
-The Agent Framework Python SDK provides multiple chat client implementations for different providers (OpenAI, Anthropic, Azure AI, Bedrock, Ollama, etc.). Each provider has unique configuration options beyond the common parameters defined in `ChatOptions`. Currently, developers using these clients lack type safety and IDE autocompletion for provider-specific options, leading to runtime errors and a poor developer experience.
-
-How can we provide type-safe, discoverable options for each chat client while maintaining a consistent API across all implementations?
-
-## Decision Drivers
-
-- **Type Safety**: Developers should get compile-time/static analysis errors when using invalid options
-- **IDE Support**: Full autocompletion and inline documentation for all available options
-- **Extensibility**: Users should be able to define custom options that extend provider-specific options
-- **Consistency**: All chat clients should follow the same pattern for options handling
-- **Provider Flexibility**: Each provider can expose its unique options without affecting the common interface
-
-## Considered Options
-
-- **Option 1: Status Quo - Class `ChatOptions` with `**kwargs`**
-- **Option 2: TypedDict with Generic Type Parameters**
-
-### Option 1: Status Quo - Class `ChatOptions` with `**kwargs`
-
-The current approach uses a base `ChatOptions` Class with common parameters, and provider-specific options are passed via `**kwargs` or loosely typed dictionaries.
-
-```python
-# Current usage - no type safety for provider-specific options
-response = await client.get_response(
-    messages=messages,
-    temperature=0.7,
-    top_k=40,
-    random=42, # No validation
-)
-```
-
-**Pros:**
-- Simple implementation
-- Maximum flexibility
-
-**Cons:**
-- No type checking for provider-specific options
-- No IDE autocompletion for available options
-- Runtime errors for typos or invalid options
-- Documentation must be consulted for each provider
-
-### Option 2: TypedDict with Generic Type Parameters (Chosen)
-
-Each chat client is parameterized with a TypeVar bound to a provider-specific `TypedDict` that extends `ChatOptions`. This enables full type safety and IDE support.
-
-```python
-# Provider-specific TypedDict
-class AnthropicChatOptions(ChatOptions, total=False):
-    """Anthropic-specific chat options."""
-    top_k: int
-    thinking: ThinkingConfig
-    # ... other Anthropic-specific options
-
-# Generic chat client
-class AnthropicChatClient(ChatClientBase[TAnthropicChatOptions]):
-    ...
-
-client = AnthropicChatClient(...)
-
-# Usage with full type safety
-response = await client.get_response(
-    messages=messages,
-    options={
-        "temperature": 0.7,
-        "top_k": 40,
-        "random": 42,  # fails type checking and IDE would flag this
-    }
-)
-
-# Users can extend for custom options
-class MyAnthropicOptions(AnthropicChatOptions, total=False):
-    custom_field: str
-
-
-client = AnthropicChatClient[MyAnthropicOptions](...)
-
-# Usage of custom options with full type safety
-response = await client.get_response(
-    messages=messages,
-    options={
-        "temperature": 0.7,
-        "top_k": 40,
-        "custom_field": "value",
-    }
-)
-
-```
-
-**Pros:**
-- Full type safety with static analysis
-- IDE autocompletion for all options
-- Compile-time error detection
-- Self-documenting through type hints
-- Users can extend options for their specific needs or advances in models
-
-**Cons:**
-- More complex implementation
-- Some type: ignore comments needed for TypedDict field overrides
-- Minor: Requires TypeVar with default (Python 3.13+ or typing_extensions)
-
-> [NOTE!]
-> In .NET this is already achieved through overloads on the `GetResponseAsync` method for each provider-specific options class, e.g., `AnthropicChatOptions`, `OpenAIChatOptions`, etc. So this does not apply to .NET.
-
-### Implementation Details
-
-1. **Base Protocol**: `ChatClientProtocol[TOptions]` is generic over options type, with default set to `ChatOptions` (the new TypedDict)
-2. **Provider TypedDicts**: Each provider defines its options extending `ChatOptions`
-    They can even override fields with type=None to indicate they are not supported.
-3. **TypeVar Pattern**: `TProviderOptions = TypeVar("TProviderOptions", bound=TypedDict, default=ProviderChatOptions, contravariant=True)`
-4. **Option Translation**: Common options are kept in place,and explicitly documented in the Options class how they are used. (e.g., `user` → `metadata.user_id`) in `_prepare_options` (for Anthropic) to preserve easy use of common options.
-
-## Decision Outcome
-
-Chosen option: **"Option 2: TypedDict with Generic Type Parameters"**, because it provides full type safety, excellent IDE support with autocompletion, and allows users to extend provider-specific options for their use cases. Extended this Generic to ChatAgents in order to also properly type the options used in agent construction and run methods.
-
-See [typed_options.py](../../python/samples/getting_started/chat_client/typed_options.py) for a complete example demonstrating the usage of typed options with custom extensions.

docs/decisions/0013-python-get-response-simplification.md

@@ -1,258 +0,0 @@
----
-status: Accepted
-contact: eavanvalkenburg
-date: 2026-01-06
-deciders: markwallace-microsoft, dmytrostruk, taochenosu, alliscode, moonbox3, sphenry
-consulted: sergeymenshykh, rbarreto, dmytrostruk, westey-m
-informed:
----
-
-# Simplify Python Get Response API into a single method
-
-## Context and Problem Statement
-
-Currently chat clients must implement two separate methods to get responses, one for streaming and one for non-streaming. This adds complexity to the client implementations and increases the maintenance burden. This was likely done because the .NET version cannot do proper typing with a single method, in Python this is possible and this for instance is also how the OpenAI python client works, this would then also make it simpler to work with the Python version because there is only one method to learn about instead of two.
-
-## Implications of this change
-
-### Current Architecture Overview
-
-The current design has **two separate methods** at each layer:
-
-| Layer | Non-streaming | Streaming |
-|-------|---------------|-----------|
-| **Protocol** | `get_response()` → `ChatResponse` | `get_streaming_response()` → `AsyncIterable[ChatResponseUpdate]` |
-| **BaseChatClient** | `get_response()` (public) | `get_streaming_response()` (public) |
-| **Implementation** | `_inner_get_response()` (private) | `_inner_get_streaming_response()` (private) |
-
-### Key Usage Areas Identified
-
-#### 1. **ChatAgent** (_agents.py)
-- `run()` → calls `self.chat_client.get_response()`
-- `run_stream()` → calls `self.chat_client.get_streaming_response()`
-
-These are parallel methods on the agent, so consolidating the client methods would **not break** the agent API. You could keep `agent.run()` and `agent.run_stream()` unchanged while internally calling `get_response(stream=True/False)`.
-
-#### 2. **Function Invocation Decorator** (_tools.py)
-This is **the most impacted area**. Currently:
-- `_handle_function_calls_response()` decorates `get_response`
-- `_handle_function_calls_streaming_response()` decorates `get_streaming_response`
-- The `use_function_invocation` class decorator wraps **both methods separately**
-
-**Impact**: The decorator logic is almost identical (~200 lines each) with small differences:
-- Non-streaming collects response, returns it
-- Streaming yields updates, returns async iterable
-
-With a unified method, you'd need **one decorator** that:
-- Checks the `stream` parameter
-- Uses `@overload` to determine return type
-- Handles both paths with conditional logic
-- The new decorator could be applied just on the method, instead of the whole class.
-
-This would **reduce code duplication** but add complexity to a single function.
-
-#### 3. **Observability/Instrumentation** (observability.py)
-Same pattern as function invocation:
-- `_trace_get_response()` wraps `get_response`
-- `_trace_get_streaming_response()` wraps `get_streaming_response`
-- `use_instrumentation` decorator applies both
-
-**Impact**: Would need consolidation into a single tracing wrapper.
-
-#### 4. **Chat Middleware** (_middleware.py)
-The `use_chat_middleware` decorator also wraps both methods separately with similar logic.
-
-#### 5. **AG-UI Client** (_client.py)
-Wraps both methods to unwrap server function calls:
-```python
-original_get_streaming_response = chat_client.get_streaming_response
-original_get_response = chat_client.get_response
-```
-
-#### 6. **Provider Implementations** (all subpackages)
-All subclasses implement both `_inner_*` methods, except:
-- OpenAI Assistants Client (and similar clients, such as Foundry Agents V1) - it implements `_inner_get_response` by calling `_inner_get_streaming_response`
-
-### Implications of Consolidation
-
-| Aspect | Impact |
-|--------|--------|
-| **Type Safety** | Overloads work well: `@overload` with `Literal[True]` → `AsyncIterable`, `Literal[False]` → `ChatResponse`. Runtime return type based on `stream` param. |
-| **Breaking Change** | **Major breaking change** for anyone implementing custom chat clients. They'd need to update from 2 methods to 1 (or 2 inner methods to 1). |
-| **Decorator Complexity** | All 3 decorator systems (function invocation, middleware, observability) would need refactoring to handle both paths in one wrapper. |
-| **Code Reduction** | Significant reduction in _tools.py (~200 lines of near-duplicate code) and other decorators. |
-| **Samples/Tests** | Many samples call `get_streaming_response()` directly - would need updates. |
-| **Protocol Simplification** | `ChatClientProtocol` goes from 2 methods + 1 property to 1 method + 1 property. |
-
-### Recommendation
-
-The consolidation makes sense architecturally, but consider:
-
-1. **The overload pattern with `stream: bool`** works well in Python typing:
-   ```python
-   @overload
-   async def get_response(self, messages, *, stream: Literal[True] = True, ...) -> AsyncIterable[ChatResponseUpdate]: ...
-   @overload
-   async def get_response(self, messages, *, stream: Literal[False] = False, ...) -> ChatResponse: ...
-   ```
-
-2. **The decorator complexity** is the biggest concern. The current approach of separate decorators for separate methods is cleaner than conditional logic inside one wrapper.
-
-## Decision Drivers
-
-- Reduce code needed to implement a Chat Client, simplify the public API for chat clients
-- Reduce code duplication in decorators and middleware
-- Maintain type safety and clarity in method signatures
-
-## Considered Options
-
-1. Status quo: Keep separate methods for streaming and non-streaming
-2. Consolidate into a single `get_response` method with a `stream` parameter
-3. Option 2 plus merging `agent.run` and `agent.run_stream` into a single method with a `stream` parameter as well
-
-## Option 1: Status Quo
-- Good: Clear separation of streaming vs non-streaming logic
-- Good: Aligned with .NET design, although it is already `run` for Python and `RunAsync` for .NET
-- Bad: Code duplication in decorators and middleware
-- Bad: More complex client implementations
-
-## Option 2: Consolidate into Single Method
-- Good: Simplified public API for chat clients
-- Good: Reduced code duplication in decorators
-- Good: Smaller API footprint for users to get familiar with
-- Good: People using OpenAI directly already expect this pattern
-- Bad: Increased complexity in decorators and middleware
-- Bad: Less alignment with .NET design (`get_response(stream=True)` vs `GetStreamingResponseAsync`)
-
-## Option 3: Consolidate + Merge Agent and Workflow Methods
-- Good: Further simplifies agent and workflow implementation
-- Good: Single method for all chat interactions
-- Good: Smaller API footprint for users to get familiar with
-- Good: People using OpenAI directly already expect this pattern
-- Good: Workflows internally already use a single method (_run_workflow_with_tracing), so would eliminate public API duplication as well, with hardly any code changes
-- Bad: More breaking changes for agent users
-- Bad: Increased complexity in agent implementation
-- Bad: More extensive misalignment with .NET design (`run(stream=True)` vs `RunStreamingAsync` in addition to `get_response` change)
-
-## Misc
-
-Smaller questions to consider:
-- Should default be `stream=False` or `stream=True`? (Current is False)
-    - Default to `False` makes it simpler for new users, as non-streaming is easier to handle.
-    - Default to `False` aligns with existing behavior.
-    - Streaming tends to be faster, so defaulting to `True` could improve performance for common use cases.
-    - Should this differ between ChatClient, Agent and Workflows? (e.g., Agent and Workflow defaults to streaming, ChatClient to non-streaming)
-
-## Decision Outcome
-
-Chosen Option: **Option 3: Consolidate + Merge Agent and Workflow Methods**
-
-Since this is the most pythonic option and it reduces the API surface and code duplication the most, we will go with this option.
-We will keep the default of `stream=False` for all methods to maintain backward compatibility and simplicity for new users.
-
-# Appendix
-## Code Samples for Consolidated Method
-
-### Python - Option 3: Direct ChatClient + Agent with Single Method
-
-```python
-# Copyright (c) Microsoft. All rights reserved.
-
-import asyncio
-from random import randint
-from typing import Annotated
-
-from agent_framework import ChatAgent
-from agent_framework.openai import OpenAIChatClient
-from pydantic import Field
-
-
-def get_weather(
-    location: Annotated[str, Field(description="The location to get the weather for.")],
-) -> str:
-    """Get the weather for a given location."""
-    conditions = ["sunny", "cloudy", "rainy", "stormy"]
-    return f"The weather in {location} is {conditions[randint(0, 3)]} with a high of {randint(10, 30)}°C."
-
-
-async def main() -> None:
-    # Example 1: Direct ChatClient usage with single method
-    client = OpenAIChatClient()
-    message = "What's the weather in Amsterdam and in Paris?"
-
-    # Non-streaming usage
-    print(f"User: {message}")
-    response = await client.get_response(message, tools=get_weather)
-    print(f"Assistant: {response.text}")
-
-    # Streaming usage - same method, different parameter
-    print(f"\nUser: {message}")
-    print("Assistant: ", end="")
-    async for chunk in client.get_response(message, tools=get_weather, stream=True):
-        if chunk.text:
-            print(chunk.text, end="")
-    print("")
-
-    # Example 2: Agent usage with single method
-    agent = ChatAgent(
-        chat_client=client,
-        tools=get_weather,
-        name="WeatherAgent",
-        instructions="You are a weather assistant.",
-    )
-    thread = agent.get_new_thread()
-
-    # Non-streaming agent
-    print(f"\nUser: {message}")
-    result = await agent.run(message, thread=thread) # default would be stream=False
-    print(f"{agent.name}: {result.text}")
-
-    # Streaming agent - same method, different parameter
-    print(f"\nUser: {message}")
-    print(f"{agent.name}: ", end="")
-    async for update in agent.run(message, thread=thread, stream=True):
-        if update.text:
-            print(update.text, end="")
-    print("")
-
-
-if __name__ == "__main__":
-    asyncio.run(main())
-```
-
-### .NET - Current pattern for comparison
-
-```csharp
-// Copyright (c) Microsoft. All rights reserved.
-
-using Azure.AI.OpenAI;
-using Azure.Identity;
-using Microsoft.Agents.AI;
-using OpenAI.Chat;
-
-var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT")
-    ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
-var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
-
-AIAgent agent = new AzureOpenAIClient(
-    new Uri(endpoint),
-    new AzureCliCredential())
-    .GetChatClient(deploymentName)
-    .CreateAIAgent(
-        instructions: "You are good at telling jokes about pirates.",
-        name: "PirateJoker");
-
-// Non-streaming: Returns a string directly
-Console.WriteLine("=== Non-streaming ===");
-string result = await agent.RunAsync("Tell me a joke about a pirate.");
-Console.WriteLine(result);
-
-// Streaming: Returns IAsyncEnumerable<AgentUpdate>
-Console.WriteLine("\n=== Streaming ===");
-await foreach (AgentUpdate update in agent.RunStreamingAsync("Tell me a joke about a pirate."))
-{
-    Console.Write(update);
-}
-Console.WriteLine();
-
-```

docs/decisions/0014-feature-collections.md

@@ -1,423 +0,0 @@
----
-status: accepted
-contact: westey-m
-date: 2025-01-21
-deciders: sergeymenshykh, markwallace, rbarreto, westey-m, stephentoub
-consulted: reubenbond
-informed:
----
-
-# Feature Collections
-
-## Context and Problem Statement
-
-When using agents, we often have cases where we want to pass some arbitrary services or data to an agent or some component in the agent execution stack.
-These services or data are not necessarily known at compile time and can vary by the agent stack that the user has built.
-E.g., there may be an agent decorator or chat client decorator that was added to the stack by the user, and an arbitrary payload needs to be passed to that decorator.
-
-Since these payloads are related to components that are not integral parts of the agent framework, they cannot be added as strongly typed settings to the agent run options.
-However, the payloads could be added to the agent run options as loosely typed 'features', that can be retrieved as needed.
-
-In some cases certain classes of agents may support the same capability, but not all agents do.
-Having the configuration for such a capability on the main abstraction would advertise the functionality to all users, even if their chosen agent does not support it.
-The user may type test for certain agent types, and call overloads on the appropriate agent types, with the strongly typed configuration.
-Having a feature collection though, would be an alternative way of passing such configuration, without needing to type check the agent type.
-All agents that support the functionality would be able to check for the configuration and use it, simplifying the user code.
-If the agent does not support the capability, that configuration would be ignored.
-
-### Sample Scenario 1 - Per Run ChatMessageStore Override for hosting Libraries
-
-We are building an agent hosting library, that can host any agent built using the agent framework.
-Where an agent is not built on a service that uses in-service chat history storage, the hosting library wants to force the agent to use
-the hosting library's chat history storage implementation.
-This chat history storage implementation may be specifically tailored to the type of protocol that the hosting library uses, e.g. conversation id based storage or response id based storage.
-The hosting library does not know what type of agent it is hosting, so it cannot provide a strongly typed parameter on the agent.
-Instead, it adds the chat history storage implementation to a feature collection, and if the agent supports custom chat history storage, it retrieves the implementation from the feature collection and uses it.
-
-```csharp
-// Pseudo-code for an agent hosting library that supports conversation id based hosting.
-public async Task<string> HandleConversationsBasedRequestAsync(AIAgent agent, string conversationId, string userInput)
-{
-    var thread = await this._threadStore.GetOrCreateThread(conversationId);
-
-    // The hosting library can set a per-run chat message store via Features that only applies for that run.
-    // This message store will load and save messages under the conversation id provided.
-    ConversationsChatMessageStore messageStore = new(this._dbClient, conversationId);
-    var response = await agent.RunAsync(
-        userInput,
-        thread,
-        options: new AgentRunOptions()
-        {
-            Features = new AgentFeatureCollection().WithFeature<ChatMessageStore>(messageStore)
-        });
-
-    await this._threadStore.SaveThreadAsync(conversationId, thread);
-    return response.Text;
-}
-
-// Pseudo-code for an agent hosting library that supports response id based hosting.
-public async Task<(string responseMessage, string responseId)> HandleResponseIdBasedRequestAsync(AIAgent agent, string previousResponseId, string userInput)
-{
-    var thread = await this._threadStore.GetOrCreateThreadAsync(previousResponseId);
-
-    // The hosting library can set a per-run chat message store via Features that only applies for that run.
-    // This message store will buffer newly added messages until explicitly saved after the run.
-    ResponsesChatMessageStore messageStore = new(this._dbClient, previousResponseId);
-
-    var response = await agent.RunAsync(
-        userInput,
-        thread,
-        options: new AgentRunOptions()
-        {
-            Features = new AgentFeatureCollection().WithFeature<ChatMessageStore>(messageStore)
-        });
-
-    // Since the message store may not actually have been used at all (if the agent's underlying chat client requires service-based chat history storage),
-    // we may not have anything to save back to the database.
-    // We still want to generate a new response id though, so that we can save the updated thread state under that id.
-    // We should also use the same id to save any buffered messages in the message store if there are any.
-    var newResponseId = this.GenerateResponseId();
-    if (messageStore.HasBufferedMessages)
-    {
-        await messageStore.SaveBufferedMessagesAsync(newResponseId);
-    }
-    
-    // Save the updated thread state under the new response id that was generated by the store.
-    await this._threadStore.SaveThreadAsync(newResponseId, thread);
-    return (response.Text, newResponseId);
-}
-```
-
-### Sample Scenario 2 - Structured output
-
-Currently our base abstraction does not support structured output, since the capability is not supported by all agents.
-For those agents that don't support structured output, we could add an agent decorator that takes the response from the underlying agent, and applies structured output parsing on top of it via an additional LLM call.
-
-If we add structured output configuration as a feature, then any agent that supports structured output could retrieve the configuration from the feature collection and apply it, and where it is not supported, the configuration would simply be ignored.
-
-We could add a simple StructuredOutputAgentFeature that can be added to the list of features and also be used to return the generated structured output.
-
-```csharp
-internal class StructuredOutputAgentFeature
-{
-    public Type? OutputType { get; set; }
-
-    public JsonSerializerOptions? SerializerOptions { get; set; }
-
-    public bool? UseJsonSchemaResponseFormat { get; set; }
-
-    // Contains the result of the structured output parsing request.
-    public ChatResponse? ChatResponse { get; set; }
-}
-```
-
-We can add a simple decorator class that does the chat client invocation.
-
-```csharp
-public class StructuredOutputAgent : DelegatingAIAgent
-{
-    private readonly IChatClient _chatClient;
-    public StructuredOutputAgent(AIAgent innerAgent, IChatClient chatClient)
-        : base(innerAgent)
-    {
-        this._chatClient = Throw.IfNull(chatClient);
-    }
-
-    public override async Task<AgentRunResponse> RunAsync(
-        IEnumerable<ChatMessage> messages,
-        AgentThread? thread = null,
-        AgentRunOptions? options = null,
-        CancellationToken cancellationToken = default)
-    {
-        // Run the inner agent first, to get back the text response we want to convert.
-        var response = await base.RunAsync(messages, thread, options, cancellationToken).ConfigureAwait(false);
-
-        if (options?.Features?.TryGet<StructuredOutputAgentFeature>(out var responseFormatFeature) is true
-            && responseFormatFeature.OutputType is not null)
-        {
-            // Create the chat options to request structured output.
-            ChatOptions chatOptions = new()
-            {
-                ResponseFormat = ChatResponseFormat.ForJsonSchema(responseFormatFeature.OutputType, responseFormatFeature.SerializerOptions)
-            };
-
-            // Invoke the chat client to transform the text output into structured data.
-            // The feature is updated with the result.
-            // The code can be simplified by adding a non-generic structured output GetResponseAsync
-            // overload that takes Type as input.
-            responseFormatFeature.ChatResponse = await this._chatClient.GetResponseAsync(
-                messages: new[]
-                {
-                    new ChatMessage(ChatRole.System, "You are a json expert and when provided with any text, will convert it to the requested json format."),
-                    new ChatMessage(ChatRole.User, response.Text)
-                },
-                options: chatOptions,
-                cancellationToken: cancellationToken).ConfigureAwait(false);
-        }
-
-        return response;
-    }
-}
-```
-
-Finally, we can add an extension method on `AIAgent` that can add the feature to the run options and check the feature for the structured output result and add the deserialized result to the response.
-
-```csharp
-public static async Task<AgentRunResponse<T>> RunAsync<T>(
-    this AIAgent agent,
-    IEnumerable<ChatMessage> messages,
-    AgentThread? thread = null,
-    JsonSerializerOptions? serializerOptions = null,
-    AgentRunOptions? options = null,
-    bool? useJsonSchemaResponseFormat = null,
-    CancellationToken cancellationToken = default)
-{
-    // Create the structured output feature.
-    var structuredOutputFeature = new StructuredOutputAgentFeature();
-    structuredOutputFeature.OutputType = typeof(T);
-    structuredOutputFeature.UseJsonSchemaResponseFormat = useJsonSchemaResponseFormat;
-
-    // Run the agent.
-    options ??= new AgentRunOptions();
-    options.Features ??= new AgentFeatureCollection();
-    options.Features.Set(structuredOutputFeature);
-
-    var response = await agent.RunAsync(messages, thread, options, cancellationToken).ConfigureAwait(false);
-
-    // Deserialize the JSON output.
-    if (structuredOutputFeature.ChatResponse is not null)
-    {
-        var typed = new ChatResponse<T>(structuredOutputFeature.ChatResponse, serializerOptions ?? AgentJsonUtilities.DefaultOptions);
-        return new AgentRunResponse<T>(response, typed.Result);
-    }
-
-    throw new InvalidOperationException("No structured output response was generated by the agent.");
-}
-```
-
-We can then use the extension method with any agent that supports structured output or that has
-been decorated with the `StructuredOutputAgent` decorator.
-
-```csharp
-agent = new StructuredOutputAgent(agent, chatClient);
-
-AgentRunResponse<PersonInfo> response = await agent.RunAsync<PersonInfo>([new ChatMessage(
-    ChatRole.User,
-    "Please provide information about John Smith, who is a 35-year-old software engineer.")]);
-```
-
-## Implementation Options
-
-Three options were considered for implementing feature collections:
-
-- **Option 1**: FeatureCollections similar to ASP.NET Core
-- **Option 2**: AdditionalProperties Dictionary
-- **Option 3**: IServiceProvider
-
-Here are some comparisons about their suitability for our use case:
-
-| Criteria         | Feature Collection | Additional Properties | IServiceProvider |
-|------------------|--------------------|-----------------------|------------------|
-|Ease of use       |✅ Good             |❌ Bad                |✅ Good           |
-|User familiarity  |❌ Bad              |✅ Good               |✅ Good           |
-|Type safety       |✅ Good             |❌ Bad                |✅ Good           |
-|Ability to modify registered options when progressing down the stack|✅ Supported|✅ Supported|❌ Not-Supported (IServiceProvider is read-only)|
-|Already available in MEAI stack|❌ No|✅ Yes|❌ No|
-|Ambiguity with existing AdditionalProperties|❌ Yes|✅ No|❌ Yes|
-
-## IServiceProvider
-
-Service Collections and Service Providers provide a very popular way to register and retrieve services by type and could be used as a way to pass features to agents and chat clients.
-
-However, since IServiceProvider is read-only, it is not possible to modify the registered services when progressing down the execution stack.
-E.g. an agent decorator cannot add additional services to the IServiceProvider passed to it when calling into the inner agent.
-
-IServiceProvider also does not expose a way to list all services contained in it, making it difficult to copy services from one provider to another.
-
-This lack of mutability makes IServiceProvider unsuitable for our use case, since we will not be able to use it to build sample scenario 2.
-
-## AdditionalProperties dictionary
-
-The AdditionalProperties dictionary is already available on various options classes in the agent framework as well as in the MEAI stack and
-allows storing arbitrary key/value pairs, where the key is a string and the value is an object.
-
-While FeatureCollection uses Type as a key, AdditionalProperties uses string keys.
-This means that users need to agree on string keys to use for specific features, however it is also possible to use Type.FullName as a key by convention
-to avoid key collisions, which is an easy convention to follow.
-
-Since the value of AdditionalProperties is of type object, users need to cast the value to the expected type when retrieving it, which is also
-a drawback, but when using the convention of using Type.FullName as a key, there is at least a clear expectation of what type to cast to.
-
-```csharp
-// Setting a feature
-options.AdditionalProperties[typeof(MyFeature).FullName] = new MyFeature();
-
-// Retrieving a feature
-if (options.AdditionalProperties.TryGetValue(typeof(MyFeature).FullName, out var featureObj)
-    && featureObj is MyFeature myFeature)
-{
-    // Use myFeature
-}
-```
-
-It would also be possible to add extension methods to simplify setting and getting features from AdditionalProperties.
-Having a base class for features should help make this more feature rich.
-
-```csharp
-// Setting a feature, this can use Type.FullName as the key.
-options.AdditionalProperties
-    .WithFeature(new MyFeature());
-
-// Retrieving a feature, this can use Type.FullName as the key.
-if (options.AdditionalProperties.TryGetFeature<MyFeature>(out var myFeature))
-{
-    // Use myFeature
-}
-```
-
-It would also be possible to add extension methods for a feature to simplify setting and getting features from AdditionalProperties.
-
-```csharp
-// Setting a feature
-options.AdditionalProperties
-    .WithMyFeature(new MyFeature());
-// Retrieving a feature
-if (options.AdditionalProperties.TryGetMyFeature(out var myFeature))
-{
-    // Use myFeature
-}
-```
-
-## Feature Collection
-
-If we choose the feature collection option, we need to decide on the design of the feature collection itself.
-
-### Feature Collections extension points
-
-We need to decide the set of actions that feature collections would be supported for. Here is the suggested list of actions:
-
-**MAAI.AIAgent:**
-
-1. GetNewThread
-    1. E.g. this would allow passing an already existing storage id for the thread to use, or an initialized custom chat message store to use.
-1. DeserializeThread
-    1. E.g. this would allow passing an already existing storage id for the thread to use, or an initialized custom chat message store to use.
-1. Run / RunStreaming
-    1. E.g. this would allow passing an override chat message store just for that run, or a desired schema for a structured output middleware component.
-
-**MEAI.ChatClient:**
-
-1. GetResponse / GetStreamingResponse
-
-### Reconciling with existing AdditionalProperties
-
-If we decide to add feature collections, separately from the existing AdditionalProperties dictionaries, we need to consider how to explain to users when to use each one.
-One possible approach though is to have the one use the other under the hood.
-AdditionalProperties could be stored as a feature in the feature collection.
-
-Users would be able to retrieve additional properties from the feature collection, in addition to retrieving it via a dedicated AdditionalProperties property.
-E.g. `features.Get<AdditionalPropertiesDictionary>()`
-
-One challenge with this approach is that when setting a value in the AdditionalProperties dictionary, the feature collection would need to be created first if it does not already exist.
-
-```csharp
-public class AgentRunOptions
-{
-    public AdditionalPropertiesDictionary? AdditionalProperties { get; set; }
-    public IAgentFeatureCollection? Features { get; set; }
-}
-
-var options = new AgentRunOptions();
-// This would need to create the feature collection first, if it does not already exist.
-options.AdditionalProperties ??= new AdditionalPropertiesDictionary();
-```
-
-Since IAgentFeatureCollection is an interface, AgentRunOptions would need to have a concrete implementation of the interface to create, meaning that the user cannot decide.
-It also means that if the user doesn't realise that AdditionalProperties is implemented using feature collections, they may set a value on AdditionalProperties, and then later overwrite the entire feature collection, losing the AdditionalProperties feature.
-
-Options to avoid these issues:
-
-1. Make `Features` readonly.
-    1. This would prevent the user from overwriting the feature collection after setting AdditionalProperties.
-    1. Since the user cannot set their own implementation of IAgentFeatureCollection, having an interface for it may not be necessary.
-
-### Feature Collection Implementation
-
-We have two options for implementing feature collections:
-
-1. Create our own [IAgentFeatureCollection interface](https://github.com/microsoft/agent-framework/pull/2354/files#diff-9c42f3e60d70a791af9841d9214e038c6de3eebfc10e3997cb4cdffeb2f1246d) and [implementation](https://github.com/microsoft/agent-framework/pull/2354/files#diff-a435cc738baec500b8799f7f58c1538e3bb06c772a208afc2615ff90ada3f4ca).
-2. Reuse the asp.net [IFeatureCollection interface](https://github.com/dotnet/aspnetcore/blob/main/src/Extensions/Features/src/IFeatureCollection.cs) and [implementation](https://github.com/dotnet/aspnetcore/blob/main/src/Extensions/Features/src/FeatureCollection.cs).
-
-#### Roll our own
-
-Advantages:
-
-Creating our own IAgentFeatureCollection interface and implementation has the advantage of being more clearly associated with the agent framework and allows us to
-improve on some of the design decisions made in asp.net core's IFeatureCollection.
-
-Drawbacks:
-
-It would mean a different implementation to maintain and test.
-
-#### Reuse asp.net IFeatureCollection
-
-Advantages:
-
-Reusing the asp.net IFeatureCollection has the advantage of being able to reuse the well-established and tested implementation from asp.net
-core. Users who are using agents in an asp.net core application may be able to pass feature collections from asp.net core to the agent framework directly.
-
-Drawbacks:
-
-While the package name is `Microsoft.Extensions.Features`, the namespaces of the types are `Microsoft.AspNetCore.Http.Features`, which may create confusion for users of agent framework who are not building web applications or services.
-Users may rightly ask: Why do I need to use a class from asp.net core when I'm not building a web application / service?
-
-The current design has some design issues that would be good to avoid.  E.g. it does not distinguish between a feature being "not set" and "null". Get returns both as null and there is no tryget method.
-Since the [default implementation](https://github.com/dotnet/aspnetcore/blob/main/src/Extensions/Features/src/FeatureCollection.cs) also supports value types, it throws for null values of value types.
-A TryGet method would be more appropriate.
-
-## Feature Layering
-
-One possible scenario when adding support for feature collections is to allow layering of features by scope.
-
-The following levels of scope could be supported:
-
-1. Application - Application wide features that apply to all agents / chat clients
-2. Artifact (Agent / ChatClient) - Features that apply to all runs of a specific agent or chat client instance
-3. Action (GetNewThread / Run / GetResponse) - Feature that apply to a single action only
-
-When retrieving a feature from the collection, the search would start from the most specific scope (Action) and progress to the least specific scope (Application), returning the first matching feature found.
-
-Introducing layering adds some challenges:
-
-- There may be multiple feature collections at the same scope level, e.g. an Agent that uses a ChatClient where both have their own feature collections.
-  - Do we layer the agent feature collection over the chat client feature collection (Application -> ChatClient -> Agent -> Run), or only use the agent feature collection in the agent (Application -> Agent -> Run), and the chat client feature collection in the chat client (Application -> ChatClient -> Run)?
-- The appropriate base feature collection may change when progressing down the stack, e.g. when an Agent calls a ChatClient, the action feature collection stays the same, but the artifact feature collection changes.
-- Who creates the feature collection hierarchy?
-  - Since the hierarchy changes as it progresses down the execution stack, and the caller can only pass in the action level feature collection, the callee needs to combine it with its own artifact level feature collection and the application level feature collection. Each action will need to build the appropriate feature collection hierarchy, at the start of its execution.
-- For Artifact level features, it seems odd to pass them in as a bag of untyped features, when we are constructing a known artifact type and therefore can have typed settings.
-  - E.g. today we have a strongly typed setting on ChatClientAgentOptions to configure a ChatMessageStore for the agent.
-- To avoid global statics for application level features, the user would need to pass in the application level feature collection to each artifact that they create.
-  - This would be very odd if the user also already has to strongly typed settings for each feature that they want to set at the artifact level.
-
-### Layering Options
-
-1. No layering - only a single feature collection is supported per action (the caller can still create a layered collection if desired, but the callee does not do any layering automatically).
-    1. Fallback is to any features configured on the artifact via strongly typed settings.
-1. Full layering - support layering at all levels (Application -> Artifact -> Action).
-    1. Only apply applicable artifact level features when calling into that artifact.
-    1. Apply upstream artifact features when calling into downstream artifacts, e.g. Feature hierarchy in ChatClientAgent would be `Application -> Agent -> Run` and in ChatClient would be `Application -> ChatClient -> Agent -> Run` or `Application -> Agent -> ChatClient -> Run`
-    1. The user needs to provide the application level feature collection to each artifact that they create and artifact features are passed via strongly typed settings.
-
-### Accessing application level features Options
-
-We need to consider how application level features would be accessed if supported.
-
-1. The user provides the application level feature collection to each artifact that the user constructs
-    1. Passing the application level feature collection to each artifact is tedious for the user.
-1. There is a static application level feature collection that can be accessed globally.
-    1. Statics create issues with testing and isolation.
-
-## Decisions
-
-- Feature Collections Container: Use AdditionalProperties
-- Feature Layering: No layering - only a single collection/dictionary is supported per action. Application layers can be added later if needed.

docs/specs/001-foundry-sdk-alignment.md

@@ -125,7 +125,7 @@ The proposed solution is to add helper methods which allow developers to either
 - [Foundry SDK] Create a `PersistentAgentsClient`
 - [Foundry SDK] Create a `PersistentAgent` using the `PersistentAgentsClient`
 - [Foundry SDK] Retrieve an `AIAgent` using the `PersistentAgentsClient`
-- [Agent Framework SDK] Invoke the `AIAgent` instance and access response from the `AgentResponse` 
+- [Agent Framework SDK] Invoke the `AIAgent` instance and access response from the `AgentRunResponse` 
 - [Foundry SDK] Clean up the agent
 
 
@@ -156,7 +156,7 @@ await persistentAgentsClient.Administration.DeleteAgentAsync(agent.Id);
 
 - [Foundry SDK] Create a `PersistentAgentsClient`
 - [Foundry SDK] Create a `AIAgent` using the `PersistentAgentsClient`
-- [Agent Framework SDK] Invoke the `AIAgent` instance and access response from the `AgentResponse` 
+- [Agent Framework SDK] Invoke the `AIAgent` instance and access response from the `AgentRunResponse` 
 - [Foundry SDK] Clean up the agent
 
 ```csharp
@@ -184,7 +184,7 @@ await persistentAgentsClient.Administration.DeleteAgentAsync(agent.Id);
 - [Foundry SDK] Create a `PersistentAgentsClient`
 - [Foundry SDK] Create a `AIAgent` using the `PersistentAgentsClient`
 - [Agent Framework SDK] Optionally create an `AgentThread` for the agent run
-- [Agent Framework SDK] Invoke the `AIAgent` instance and access response from the `AgentResponse` 
+- [Agent Framework SDK] Invoke the `AIAgent` instance and access response from the `AgentRunResponse` 
 - [Foundry SDK] Clean up the agent and the agent thread
 
 ```csharp
@@ -227,7 +227,7 @@ await persistentAgentsClient.Administration.DeleteAgentAsync(agent.Id);
 - [Foundry SDK] Create a `PersistentAgentsClient`
 - [Foundry SDK] Create multiple `AIAgent` instances using the `PersistentAgentsClient`
 - [Agent Framework SDK] Create a `SequentialOrchestration` and add all of the agents to it
-- [Agent Framework SDK] Invoke the `SequentialOrchestration` instance and access response from the `AgentResponse` 
+- [Agent Framework SDK] Invoke the `SequentialOrchestration` instance and access response from the `AgentRunResponse` 
 - [Foundry SDK] Clean up the agents
 
 ```csharp
@@ -281,7 +281,7 @@ SequentialOrchestration orchestration =
 // Run the orchestration
 string input = "An eco-friendly stainless steel water bottle that keeps drinks cold for 24 hours";
 Console.WriteLine($"\n# INPUT: {input}\n");
-AgentResponse result = await orchestration.RunAsync(input);
+AgentRunResponse result = await orchestration.RunAsync(input);
 Console.WriteLine($"\n# RESULT: {result}");
 
 // Cleanup

dotnet/Directory.Packages.props

@@ -11,7 +11,7 @@
   </PropertyGroup>
   <ItemGroup>
     <!-- Aspire.* -->
-    <PackageVersion Include="Anthropic" Version="12.0.1" />
+    <PackageVersion Include="Anthropic" Version="12.0.0" />
     <PackageVersion Include="Anthropic.Foundry" Version="0.1.0" />
     <PackageVersion Include="Aspire.Azure.AI.OpenAI" Version="13.0.0-preview.1.25560.3" />
     <PackageVersion Include="Aspire.Hosting.AppHost" Version="$(AspireAppHostSdkVersion)" />
@@ -26,25 +26,25 @@
     <PackageVersion Include="Azure.Identity" Version="1.17.1" />
     <PackageVersion Include="Azure.Monitor.OpenTelemetry.Exporter" Version="1.4.0" />
     <!-- Google Gemini  -->
-    <PackageVersion Include="Google.GenAI" Version="0.11.0" />
+    <PackageVersion Include="Google.GenAI" Version="0.6.0" />
     <PackageVersion Include="Mscc.GenerativeAI.Microsoft" Version="2.9.3" />
     <!-- Microsoft.Azure.* -->
     <PackageVersion Include="Microsoft.Azure.Cosmos" Version="3.54.0" />
     <!-- Newtonsoft.Json -->
     <PackageVersion Include="Newtonsoft.Json" Version="13.0.4" />
     <!-- System.* -->
-    <PackageVersion Include="Microsoft.Bcl.AsyncInterfaces" Version="10.0.2" />
+    <PackageVersion Include="Microsoft.Bcl.AsyncInterfaces" Version="10.0.1" />
     <PackageVersion Include="Microsoft.Bcl.HashCode" Version="6.0.0" />
     <PackageVersion Include="System.ClientModel" Version="1.8.1" />
     <PackageVersion Include="System.CodeDom" Version="10.0.0" />
     <PackageVersion Include="System.Collections.Immutable" Version="10.0.0" />
     <PackageVersion Include="System.CommandLine" Version="2.0.0-rc.2.25502.107" />
-    <PackageVersion Include="System.Diagnostics.DiagnosticSource" Version="10.0.2" />
+    <PackageVersion Include="System.Diagnostics.DiagnosticSource" Version="10.0.1" />
     <PackageVersion Include="System.Linq.AsyncEnumerable" Version="10.0.0" />
     <PackageVersion Include="System.Net.Http.Json" Version="10.0.0" />
     <PackageVersion Include="System.Net.ServerSentEvents" Version="10.0.0" />
-    <PackageVersion Include="System.Text.Json" Version="10.0.2" />
-    <PackageVersion Include="System.Threading.Channels" Version="10.0.2" />
+    <PackageVersion Include="System.Text.Json" Version="10.0.1" />
+    <PackageVersion Include="System.Threading.Channels" Version="10.0.1" />
     <PackageVersion Include="System.Threading.Tasks.Extensions" Version="4.6.3" />
     <PackageVersion Include="System.Net.Security" Version="4.3.2" />
     <!-- OpenTelemetry -->
@@ -61,9 +61,9 @@
     <PackageVersion Include="Microsoft.AspNetCore.OpenApi" Version="10.0.0" />
     <PackageVersion Include="Swashbuckle.AspNetCore.SwaggerUI" Version="10.0.0" />
     <!-- Microsoft.Extensions.* -->
-    <PackageVersion Include="Microsoft.Extensions.AI" Version="10.2.0" />
-    <PackageVersion Include="Microsoft.Extensions.AI.Abstractions" Version="10.2.0" />
-    <PackageVersion Include="Microsoft.Extensions.AI.OpenAI" Version="10.2.0-preview.1.26063.2" />
+    <PackageVersion Include="Microsoft.Extensions.AI" Version="10.1.1" />
+    <PackageVersion Include="Microsoft.Extensions.AI.Abstractions" Version="10.1.1" />
+    <PackageVersion Include="Microsoft.Extensions.AI.OpenAI" Version="10.1.1-preview.1.25612.2" />
     <PackageVersion Include="Microsoft.Extensions.Caching.Memory" Version="10.0.0" />
     <PackageVersion Include="Microsoft.Extensions.Configuration" Version="10.0.0" />
     <PackageVersion Include="Microsoft.Extensions.Configuration.Binder" Version="10.0.0" />
@@ -71,11 +71,11 @@
     <PackageVersion Include="Microsoft.Extensions.Configuration.Json" Version="10.0.0" />
     <PackageVersion Include="Microsoft.Extensions.Configuration.UserSecrets" Version="10.0.0" />
     <PackageVersion Include="Microsoft.Extensions.DependencyInjection" Version="10.0.0" />
-    <PackageVersion Include="Microsoft.Extensions.DependencyInjection.Abstractions" Version="10.0.2" />
+    <PackageVersion Include="Microsoft.Extensions.DependencyInjection.Abstractions" Version="10.0.1" />
     <PackageVersion Include="Microsoft.Extensions.Hosting" Version="10.0.0" />
     <PackageVersion Include="Microsoft.Extensions.Http.Resilience" Version="10.0.0" />
     <PackageVersion Include="Microsoft.Extensions.Logging" Version="10.0.0" />
-    <PackageVersion Include="Microsoft.Extensions.Logging.Abstractions" Version="10.0.2" />
+    <PackageVersion Include="Microsoft.Extensions.Logging.Abstractions" Version="10.0.1" />
     <PackageVersion Include="Microsoft.Extensions.Logging.Console" Version="10.0.0" />
     <PackageVersion Include="Microsoft.Extensions.ServiceDiscovery" Version="10.0.0" />
     <PackageVersion Include="Microsoft.Extensions.VectorData.Abstractions" Version="9.7.0" />
@@ -100,7 +100,7 @@
     <!-- MCP -->
     <PackageVersion Include="ModelContextProtocol" Version="0.4.0-preview.3" />
     <!-- Inference SDKs -->
-    <PackageVersion Include="AWSSDK.Extensions.Bedrock.MEAI" Version="4.0.5.1" />
+    <PackageVersion Include="AWSSDK.Extensions.Bedrock.MEAI" Version="4.0.5" />
     <PackageVersion Include="Microsoft.ML.OnnxRuntimeGenAI" Version="0.10.0" />
     <PackageVersion Include="OllamaSharp" Version="5.4.8" />
     <PackageVersion Include="OpenAI" Version="2.8.0" />
@@ -143,7 +143,6 @@
     <!-- Symbols -->
     <PackageVersion Include="Microsoft.SourceLink.GitHub" Version="8.0.0" />
     <!-- Toolset -->
-    <PackageVersion Include="Microsoft.CodeAnalysis.Analyzers" Version="3.11.0" />
     <PackageVersion Include="Microsoft.CodeAnalysis.CSharp" Version="4.14.0" />
     <PackageVersion Include="Microsoft.CodeAnalysis.NetAnalyzers" Version="10.0.100" />
     <PackageReference Include="Microsoft.CodeAnalysis.NetAnalyzers">

dotnet/README.md

@@ -21,7 +21,7 @@ var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT
 
 var agent = new AzureOpenAIClient(new Uri(endpoint), new AzureCliCredential())
     .GetOpenAIResponseClient(deploymentName)
-    .AsAIAgent(name: "HaikuBot", instructions: "You are an upbeat assistant that writes beautifully.");
+    .CreateAIAgent(name: "HaikuBot", instructions: "You are an upbeat assistant that writes beautifully.");
 
 Console.WriteLine(await agent.RunAsync("Write a haiku about Microsoft Agent Framework."));
 ```

dotnet/agent-framework-dotnet.slnx

@@ -35,18 +35,6 @@
     <Project Path="samples/AzureFunctions/07_AgentAsMcpTool/07_AgentAsMcpTool.csproj" />
     <Project Path="samples/AzureFunctions/08_ReliableStreaming/08_ReliableStreaming.csproj" />
   </Folder>
-  <Folder Name="/Samples/DurableAgents/">
-    <File Path="samples/DurableAgents/ConsoleApps/README.md" />
-  </Folder>
-  <Folder Name="/Samples/DurableAgents/ConsoleApps/">
-    <Project Path="samples/DurableAgents/ConsoleApps/01_SingleAgent/01_SingleAgent.csproj" />
-    <Project Path="samples/DurableAgents/ConsoleApps/02_AgentOrchestration_Chaining/02_AgentOrchestration_Chaining.csproj" />
-    <Project Path="samples/DurableAgents/ConsoleApps/03_AgentOrchestration_Concurrency/03_AgentOrchestration_Concurrency.csproj" />
-    <Project Path="samples/DurableAgents/ConsoleApps/04_AgentOrchestration_Conditionals/04_AgentOrchestration_Conditionals.csproj" />
-    <Project Path="samples/DurableAgents/ConsoleApps/05_AgentOrchestration_HITL/05_AgentOrchestration_HITL.csproj" />
-    <Project Path="samples/DurableAgents/ConsoleApps/06_LongRunningTools/06_LongRunningTools.csproj" />
-    <Project Path="samples/DurableAgents/ConsoleApps/07_ReliableStreaming/07_ReliableStreaming.csproj" />
-  </Folder>
   <Folder Name="/Samples/GettingStarted/">
     <File Path="samples/GettingStarted/README.md" />
   </Folder>
@@ -93,7 +81,6 @@
     <Project Path="samples/GettingStarted/Agents/Agent_Step17_BackgroundResponses/Agent_Step17_BackgroundResponses.csproj" />
     <Project Path="samples/GettingStarted/Agents/Agent_Step18_DeepResearch/Agent_Step18_DeepResearch.csproj" />
     <Project Path="samples/GettingStarted/Agents/Agent_Step19_Declarative/Agent_Step19_Declarative.csproj" />
-    <Project Path="samples/GettingStarted/Agents/Agent_Step20_AdditionalAIContext/Agent_Step20_AdditionalAIContext.csproj" />
   </Folder>
   <Folder Name="/Samples/GettingStarted/DeclarativeAgents/">
     <Project Path="samples/GettingStarted/DeclarativeAgents/ChatClient/DeclarativeChatClientAgents.csproj" />
@@ -299,11 +286,6 @@
     <File Path="../docs/decisions/0007-agent-filtering-middleware.md" />
     <File Path="../docs/decisions/0008-python-subpackages.md" />
     <File Path="../docs/decisions/0009-support-long-running-operations.md" />
-    <File Path="../docs/decisions/0010-ag-ui-support.md" />
-    <File Path="../docs/decisions/0011-create-get-agent-api.md" />
-    <File Path="../docs/decisions/0012-python-typeddict-options.md" />
-    <File Path="../docs/decisions/0013-python-get-response-simplification.md" />
-    <File Path="../docs/decisions/0014-feature-collections.md" />
     <File Path="../docs/decisions/adr-short-template.md" />
     <File Path="../docs/decisions/adr-template.md" />
     <File Path="../docs/decisions/README.md" />
@@ -414,7 +396,6 @@
     <Project Path="src/Microsoft.Agents.AI.Workflows.Declarative.AzureAI/Microsoft.Agents.AI.Workflows.Declarative.AzureAI.csproj" />
     <Project Path="src/Microsoft.Agents.AI.Workflows.Declarative/Microsoft.Agents.AI.Workflows.Declarative.csproj" />
     <Project Path="src/Microsoft.Agents.AI.Workflows/Microsoft.Agents.AI.Workflows.csproj" />
-    <Project Path="src/Microsoft.Agents.AI.Workflows.Generators/Microsoft.Agents.AI.Workflows.Generators.csproj" />
     <Project Path="src/Microsoft.Agents.AI/Microsoft.Agents.AI.csproj" />
   </Folder>
   <Folder Name="/Tests/" />
@@ -454,7 +435,6 @@
     <Project Path="tests/Microsoft.Agents.AI.Purview.UnitTests/Microsoft.Agents.AI.Purview.UnitTests.csproj" />
     <Project Path="tests/Microsoft.Agents.AI.UnitTests/Microsoft.Agents.AI.UnitTests.csproj" />
     <Project Path="tests/Microsoft.Agents.AI.Workflows.Declarative.UnitTests/Microsoft.Agents.AI.Workflows.Declarative.UnitTests.csproj" />
-    <Project Path="tests/Microsoft.Agents.AI.Workflows.Generators.UnitTests/Microsoft.Agents.AI.Workflows.Generators.UnitTests.csproj" />
     <Project Path="tests/Microsoft.Agents.AI.Workflows.UnitTests/Microsoft.Agents.AI.Workflows.UnitTests.csproj" />
   </Folder>
 </Solution>

dotnet/nuget/nuget-package.props

@@ -2,9 +2,9 @@
   <PropertyGroup>
     <!-- Central version prefix - applies to all nuget packages. -->
     <VersionPrefix>1.0.0</VersionPrefix>
-    <PackageVersion Condition="'$(VersionSuffix)' != ''">$(VersionPrefix)-$(VersionSuffix).260121.1</PackageVersion>
-    <PackageVersion Condition="'$(VersionSuffix)' == ''">$(VersionPrefix)-preview.260121.1</PackageVersion>
-    <GitTag>1.0.0-preview.260121.1</GitTag>
+    <PackageVersion Condition="'$(VersionSuffix)' != ''">$(VersionPrefix)-$(VersionSuffix).251219.1</PackageVersion>
+    <PackageVersion Condition="'$(VersionSuffix)' == ''">$(VersionPrefix)-preview.251219.1</PackageVersion>
+    <GitTag>1.0.0-preview.251219.1</GitTag>
 
     <Configurations>Debug;Release;Publish</Configurations>
     <IsPackable>true</IsPackable>

dotnet/samples/A2AClientServer/A2AClient/HostClientAgent.cs

@@ -29,7 +29,7 @@ internal sealed class HostClientAgent
             // Create the agent that uses the remote agents as tools
             this.Agent = new OpenAIClient(new ApiKeyCredential(apiKey))
              .GetChatClient(modelId)
-             .AsAIAgent(instructions: "You specialize in handling queries for users and using your tools to provide answers.", name: "HostClient", tools: tools);
+             .CreateAIAgent(instructions: "You specialize in handling queries for users and using your tools to provide answers.", name: "HostClient", tools: tools);
         }
         catch (Exception ex)
         {

dotnet/samples/A2AClientServer/A2AClient/Program.cs

@@ -42,7 +42,7 @@ public static class Program
         // Create the Host agent
         var hostAgent = new HostClientAgent(loggerFactory);
         await hostAgent.InitializeAgentAsync(modelId, apiKey, agentUrls!.Split(";"));
-        AgentThread thread = await hostAgent.Agent!.GetNewThreadAsync(cancellationToken);
+        AgentThread thread = hostAgent.Agent!.GetNewThread();
         try
         {
             while (true)

dotnet/samples/A2AClientServer/A2AServer/HostAgentFactory.cs

@@ -35,7 +35,7 @@ internal static class HostAgentFactory
     {
         AIAgent agent = new OpenAIClient(apiKey)
              .GetChatClient(model)
-             .AsAIAgent(instructions, name, tools: tools);
+             .CreateAIAgent(instructions, name, tools: tools);
 
         AgentCard agentCard = agentType.ToUpperInvariant() switch
         {

dotnet/samples/AGUIClientServer/AGUIClient/Program.cs

@@ -83,12 +83,12 @@ public static class Program
             serverUrl,
             jsonSerializerOptions: AGUIClientSerializerContext.Default.Options);
 
-        AIAgent agent = chatClient.AsAIAgent(
+        AIAgent agent = chatClient.CreateAIAgent(
             name: "agui-client",
             description: "AG-UI Client Agent",
             tools: [changeBackground, readClientClimateSensors]);
 
-        AgentThread thread = await agent.GetNewThreadAsync(cancellationToken);
+        AgentThread thread = agent.GetNewThread();
         List<ChatMessage> messages = [new(ChatRole.System, "You are a helpful assistant.")];
         try
         {
@@ -114,7 +114,7 @@ public static class Program
                 bool isFirstUpdate = true;
                 string? threadId = null;
                 var updates = new List<ChatResponseUpdate>();
-                await foreach (AgentResponseUpdate update in agent.RunStreamingAsync(messages, thread, cancellationToken: cancellationToken))
+                await foreach (AgentRunResponseUpdate update in agent.RunStreamingAsync(messages, thread, cancellationToken: cancellationToken))
                 {
                     // Use AsChatResponseUpdate to access ChatResponseUpdate properties
                     ChatResponseUpdate chatUpdate = update.AsChatResponseUpdate();

dotnet/samples/AGUIClientServer/AGUIDojoServer/AgenticUI/AgenticUIAgent.cs

@@ -19,12 +19,12 @@ internal sealed class AgenticUIAgent : DelegatingAIAgent
         this._jsonSerializerOptions = jsonSerializerOptions;
     }
 
-    protected override Task<AgentResponse> RunCoreAsync(IEnumerable<ChatMessage> messages, AgentThread? thread = null, AgentRunOptions? options = null, CancellationToken cancellationToken = default)
+    protected override Task<AgentRunResponse> RunCoreAsync(IEnumerable<ChatMessage> messages, AgentThread? thread = null, AgentRunOptions? options = null, CancellationToken cancellationToken = default)
     {
-        return this.RunCoreStreamingAsync(messages, thread, options, cancellationToken).ToAgentResponseAsync(cancellationToken);
+        return this.RunCoreStreamingAsync(messages, thread, options, cancellationToken).ToAgentRunResponseAsync(cancellationToken);
     }
 
-    protected override async IAsyncEnumerable<AgentResponseUpdate> RunCoreStreamingAsync(
+    protected override async IAsyncEnumerable<AgentRunResponseUpdate> RunCoreStreamingAsync(
         IEnumerable<ChatMessage> messages,
         AgentThread? thread = null,
         AgentRunOptions? options = null,
@@ -69,7 +69,7 @@ internal sealed class AgenticUIAgent : DelegatingAIAgent
 
             yield return update;
 
-            yield return new AgentResponseUpdate(
+            yield return new AgentRunResponseUpdate(
                 new ChatResponseUpdate(role: ChatRole.System, stateEventsToEmit)
                 {
                     MessageId = "delta_" + Guid.NewGuid().ToString("N"),

dotnet/samples/AGUIClientServer/AGUIDojoServer/ChatClientAgentFactory.cs

@@ -33,7 +33,7 @@ internal static class ChatClientAgentFactory
     {
         ChatClient chatClient = s_azureOpenAIClient!.GetChatClient(s_deploymentName!);
 
-        return chatClient.AsIChatClient().AsAIAgent(
+        return chatClient.AsIChatClient().CreateAIAgent(
             name: "AgenticChat",
             description: "A simple chat agent using Azure OpenAI");
     }
@@ -42,7 +42,7 @@ internal static class ChatClientAgentFactory
     {
         ChatClient chatClient = s_azureOpenAIClient!.GetChatClient(s_deploymentName!);
 
-        return chatClient.AsIChatClient().AsAIAgent(
+        return chatClient.AsIChatClient().CreateAIAgent(
             name: "BackendToolRenderer",
             description: "An agent that can render backend tools using Azure OpenAI",
             tools: [AIFunctionFactory.Create(
@@ -56,7 +56,7 @@ internal static class ChatClientAgentFactory
     {
         ChatClient chatClient = s_azureOpenAIClient!.GetChatClient(s_deploymentName!);
 
-        return chatClient.AsIChatClient().AsAIAgent(
+        return chatClient.AsIChatClient().CreateAIAgent(
             name: "HumanInTheLoopAgent",
             description: "An agent that involves human feedback in its decision-making process using Azure OpenAI");
     }
@@ -65,7 +65,7 @@ internal static class ChatClientAgentFactory
     {
         ChatClient chatClient = s_azureOpenAIClient!.GetChatClient(s_deploymentName!);
 
-        return chatClient.AsIChatClient().AsAIAgent(
+        return chatClient.AsIChatClient().CreateAIAgent(
             name: "ToolBasedGenerativeUIAgent",
             description: "An agent that uses tools to generate user interfaces using Azure OpenAI");
     }
@@ -73,7 +73,7 @@ internal static class ChatClientAgentFactory
     public static AIAgent CreateAgenticUI(JsonSerializerOptions options)
     {
         ChatClient chatClient = s_azureOpenAIClient!.GetChatClient(s_deploymentName!);
-        var baseAgent = chatClient.AsIChatClient().AsAIAgent(new ChatClientAgentOptions
+        var baseAgent = chatClient.AsIChatClient().CreateAIAgent(new ChatClientAgentOptions
         {
             Name = "AgenticUIAgent",
             Description = "An agent that generates agentic user interfaces using Azure OpenAI",
@@ -116,7 +116,7 @@ internal static class ChatClientAgentFactory
     {
         ChatClient chatClient = s_azureOpenAIClient!.GetChatClient(s_deploymentName!);
 
-        var baseAgent = chatClient.AsIChatClient().AsAIAgent(
+        var baseAgent = chatClient.AsIChatClient().CreateAIAgent(
             name: "SharedStateAgent",
             description: "An agent that demonstrates shared state patterns using Azure OpenAI");
 
@@ -127,7 +127,7 @@ internal static class ChatClientAgentFactory
     {
         ChatClient chatClient = s_azureOpenAIClient!.GetChatClient(s_deploymentName!);
 
-        var baseAgent = chatClient.AsIChatClient().AsAIAgent(new ChatClientAgentOptions
+        var baseAgent = chatClient.AsIChatClient().CreateAIAgent(new ChatClientAgentOptions
         {
             Name = "PredictiveStateUpdatesAgent",
             Description = "An agent that demonstrates predictive state updates using Azure OpenAI",

dotnet/samples/AGUIClientServer/AGUIDojoServer/PredictiveStateUpdates/PredictiveStateUpdatesAgent.cs

@@ -20,12 +20,12 @@ internal sealed class PredictiveStateUpdatesAgent : DelegatingAIAgent
         this._jsonSerializerOptions = jsonSerializerOptions;
     }
 
-    protected override Task<AgentResponse> RunCoreAsync(IEnumerable<ChatMessage> messages, AgentThread? thread = null, AgentRunOptions? options = null, CancellationToken cancellationToken = default)
+    protected override Task<AgentRunResponse> RunCoreAsync(IEnumerable<ChatMessage> messages, AgentThread? thread = null, AgentRunOptions? options = null, CancellationToken cancellationToken = default)
     {
-        return this.RunCoreStreamingAsync(messages, thread, options, cancellationToken).ToAgentResponseAsync(cancellationToken);
+        return this.RunCoreStreamingAsync(messages, thread, options, cancellationToken).ToAgentRunResponseAsync(cancellationToken);
     }
 
-    protected override async IAsyncEnumerable<AgentResponseUpdate> RunCoreStreamingAsync(
+    protected override async IAsyncEnumerable<AgentRunResponseUpdate> RunCoreStreamingAsync(
         IEnumerable<ChatMessage> messages,
         AgentThread? thread = null,
         AgentRunOptions? options = null,
@@ -79,7 +79,7 @@ internal sealed class PredictiveStateUpdatesAgent : DelegatingAIAgent
                         stateUpdate,
                         this._jsonSerializerOptions.GetTypeInfo(typeof(DocumentState)));
 
-                    yield return new AgentResponseUpdate(
+                    yield return new AgentRunResponseUpdate(
                         new ChatResponseUpdate(role: ChatRole.Assistant, [new DataContent(stateBytes, "application/json")])
                         {
                             MessageId = "snapshot" + Guid.NewGuid().ToString("N"),

dotnet/samples/AGUIClientServer/AGUIDojoServer/SharedState/SharedStateAgent.cs

@@ -19,12 +19,12 @@ internal sealed class SharedStateAgent : DelegatingAIAgent
         this._jsonSerializerOptions = jsonSerializerOptions;
     }
 
-    protected override Task<AgentResponse> RunCoreAsync(IEnumerable<ChatMessage> messages, AgentThread? thread = null, AgentRunOptions? options = null, CancellationToken cancellationToken = default)
+    protected override Task<AgentRunResponse> RunCoreAsync(IEnumerable<ChatMessage> messages, AgentThread? thread = null, AgentRunOptions? options = null, CancellationToken cancellationToken = default)
     {
-        return this.RunCoreStreamingAsync(messages, thread, options, cancellationToken).ToAgentResponseAsync(cancellationToken);
+        return this.RunCoreStreamingAsync(messages, thread, options, cancellationToken).ToAgentRunResponseAsync(cancellationToken);
     }
 
-    protected override async IAsyncEnumerable<AgentResponseUpdate> RunCoreStreamingAsync(
+    protected override async IAsyncEnumerable<AgentRunResponseUpdate> RunCoreStreamingAsync(
         IEnumerable<ChatMessage> messages,
         AgentThread? thread = null,
         AgentRunOptions? options = null,
@@ -63,7 +63,7 @@ internal sealed class SharedStateAgent : DelegatingAIAgent
 
         var firstRunMessages = messages.Append(stateUpdateMessage);
 
-        var allUpdates = new List<AgentResponseUpdate>();
+        var allUpdates = new List<AgentRunResponseUpdate>();
         await foreach (var update in this.InnerAgent.RunStreamingAsync(firstRunMessages, thread, firstRunOptions, cancellationToken).ConfigureAwait(false))
         {
             allUpdates.Add(update);
@@ -76,14 +76,14 @@ internal sealed class SharedStateAgent : DelegatingAIAgent
             }
         }
 
-        var response = allUpdates.ToAgentResponse();
+        var response = allUpdates.ToAgentRunResponse();
 
         if (response.TryDeserialize(this._jsonSerializerOptions, out JsonElement stateSnapshot))
         {
             byte[] stateBytes = JsonSerializer.SerializeToUtf8Bytes(
                 stateSnapshot,
                 this._jsonSerializerOptions.GetTypeInfo(typeof(JsonElement)));
-            yield return new AgentResponseUpdate
+            yield return new AgentRunResponseUpdate
             {
                 Contents = [new DataContent(stateBytes, "application/json")]
             };

dotnet/samples/AGUIClientServer/AGUIServer/Program.cs

@@ -23,7 +23,7 @@ var agent = new AzureOpenAIClient(
         new Uri(endpoint),
         new DefaultAzureCredential())
     .GetChatClient(deploymentName)
-    .AsAIAgent(
+    .CreateAIAgent(
         name: "AGUIAssistant",
         tools: [
             AIFunctionFactory.Create(

dotnet/samples/AGUIClientServer/README.md

@@ -119,7 +119,7 @@ The `AGUIServer` uses the `MapAGUI` extension method to expose an agent through
 ```csharp
 AIAgent agent = new OpenAIClient(apiKey)
     .GetChatClient(model)
-    .AsAIAgent(
+    .CreateAIAgent(
         instructions: "You are a helpful assistant.",
         name: "AGUIAssistant");
 
@@ -144,16 +144,16 @@ var chatClient = new AGUIChatClient(
     modelId: "agui-client",
     jsonSerializerOptions: null);
 
-AIAgent agent = chatClient.AsAIAgent(
+AIAgent agent = chatClient.CreateAIAgent(
     instructions: null,
     name: "agui-client",
     description: "AG-UI Client Agent",
     tools: []);
 
 bool isFirstUpdate = true;
-AgentResponseUpdate? currentUpdate = null;
+AgentRunResponseUpdate? currentUpdate = null;
 
-await foreach (AgentResponseUpdate update in agent.RunStreamingAsync(messages, thread))
+await foreach (AgentRunResponseUpdate update in agent.RunStreamingAsync(messages, thread))
 {
     // First update indicates run started
     if (isFirstUpdate)
@@ -190,19 +190,19 @@ if (currentUpdate != null)
 The `RunStreamingAsync` method:
 1. Sends messages to the server via HTTP POST
 2. Receives server-sent events (SSE) stream
-3. Parses events into `AgentResponseUpdate` objects
+3. Parses events into `AgentRunResponseUpdate` objects
 4. Yields updates as they arrive for real-time display
 
 ## Key Concepts
 
 - **Thread**: Represents a conversation context that persists across multiple runs (accessed via `ConversationId` property)
 - **Run**: A single execution of the agent for a given set of messages (identified by `ResponseId` property)
-- **AgentResponseUpdate**: Contains the response data with:
+- **AgentRunResponseUpdate**: Contains the response data with:
   - `ResponseId`: The unique run identifier
   - `ConversationId`: The thread/conversation identifier
   - `Contents`: Collection of content items (TextContent, ErrorContent, etc.)
 - **Run Lifecycle**: 
-  - The **first** `AgentResponseUpdate` in a run indicates the run has started
+  - The **first** `AgentRunResponseUpdate` in a run indicates the run has started
   - Subsequent updates contain streaming content as the agent processes
-  - The **last** `AgentResponseUpdate` in a run indicates the run has finished
+  - The **last** `AgentRunResponseUpdate` in a run indicates the run has finished
   - If an error occurs, the update will contain `ErrorContent`

dotnet/samples/AGUIWebChat/README.md

@@ -74,7 +74,7 @@ AzureOpenAIClient azureOpenAIClient = new AzureOpenAIClient(
 ChatClient chatClient = azureOpenAIClient.GetChatClient(deploymentName);
 
 // Create AI agent
-ChatClientAgent agent = chatClient.AsIChatClient().AsAIAgent(
+ChatClientAgent agent = chatClient.AsIChatClient().CreateAIAgent(
     name: "ChatAssistant",
     instructions: "You are a helpful assistant.");
 
@@ -162,7 +162,7 @@ dotnet run
 Edit the instructions in `Server/Program.cs`:
 
 ```csharp
-ChatClientAgent agent = chatClient.AsIChatClient().AsAIAgent(
+ChatClientAgent agent = chatClient.AsIChatClient().CreateAIAgent(
     name: "ChatAssistant",
     instructions: "You are a helpful coding assistant specializing in C# and .NET.");
 ```

dotnet/samples/AGUIWebChat/Server/Program.cs

@@ -25,7 +25,7 @@ AzureOpenAIClient azureOpenAIClient = new(
 
 ChatClient chatClient = azureOpenAIClient.GetChatClient(deploymentName);
 
-ChatClientAgent agent = chatClient.AsIChatClient().AsAIAgent(
+ChatClientAgent agent = chatClient.AsIChatClient().CreateAIAgent(
     name: "ChatAssistant",
     instructions: "You are a helpful assistant.");
 

dotnet/samples/AgentWebChat/AgentWebChat.Web/A2AAgentClient.cs

@@ -25,7 +25,7 @@ internal sealed class A2AAgentClient : AgentClientBase
         this._uri = baseUri;
     }
 
-    public override async IAsyncEnumerable<AgentResponseUpdate> RunStreamingAsync(
+    public override async IAsyncEnumerable<AgentRunResponseUpdate> RunStreamingAsync(
         string agentName,
         IList<ChatMessage> messages,
         string? threadId = null,
@@ -37,7 +37,7 @@ internal sealed class A2AAgentClient : AgentClientBase
         var contextId = threadId ?? Guid.NewGuid().ToString("N");
 
         // Convert and send messages via A2A without try-catch in yield method
-        var results = new List<AgentResponseUpdate>();
+        var results = new List<AgentRunResponseUpdate>();
 
         try
         {
@@ -60,7 +60,7 @@ internal sealed class A2AAgentClient : AgentClientBase
                 var responseMessage = message.ToChatMessage();
                 if (responseMessage is { Contents.Count: > 0 })
                 {
-                    results.Add(new AgentResponseUpdate(responseMessage.Role, responseMessage.Contents)
+                    results.Add(new AgentRunResponseUpdate(responseMessage.Role, responseMessage.Contents)
                     {
                         MessageId = message.MessageId,
                         CreatedAt = DateTimeOffset.UtcNow
@@ -90,7 +90,7 @@ internal sealed class A2AAgentClient : AgentClientBase
                                 RawRepresentation = artifact,
                             };
 
-                            results.Add(new AgentResponseUpdate(chatMessage.Role, chatMessage.Contents)
+                            results.Add(new AgentRunResponseUpdate(chatMessage.Role, chatMessage.Contents)
                             {
                                 MessageId = agentTask.Id,
                                 CreatedAt = DateTimeOffset.UtcNow
@@ -108,7 +108,7 @@ internal sealed class A2AAgentClient : AgentClientBase
         {
             this._logger.LogError(ex, "Error running agent {AgentName} via A2A", agentName);
 
-            results.Add(new AgentResponseUpdate(ChatRole.Assistant, $"Error: {ex.Message}")
+            results.Add(new AgentRunResponseUpdate(ChatRole.Assistant, $"Error: {ex.Message}")
             {
                 MessageId = Guid.NewGuid().ToString("N"),
                 CreatedAt = DateTimeOffset.UtcNow

dotnet/samples/AgentWebChat/AgentWebChat.Web/IAgentClient.cs

@@ -19,7 +19,7 @@ internal abstract class AgentClientBase
     /// <param name="threadId">Optional thread identifier for conversation continuity.</param>
     /// <param name="cancellationToken">Cancellation token.</param>
     /// <returns>An asynchronous enumerable of agent response updates.</returns>
-    public abstract IAsyncEnumerable<AgentResponseUpdate> RunStreamingAsync(
+    public abstract IAsyncEnumerable<AgentRunResponseUpdate> RunStreamingAsync(
         string agentName,
         IList<ChatMessage> messages,
         string? threadId = null,

dotnet/samples/AgentWebChat/AgentWebChat.Web/OpenAIChatCompletionsAgentClient.cs

@@ -16,7 +16,7 @@ namespace AgentWebChat.Web;
 /// </summary>
 internal sealed class OpenAIChatCompletionsAgentClient(HttpClient httpClient) : AgentClientBase
 {
-    public override async IAsyncEnumerable<AgentResponseUpdate> RunStreamingAsync(
+    public override async IAsyncEnumerable<AgentRunResponseUpdate> RunStreamingAsync(
         string agentName,
         IList<ChatMessage> messages,
         string? threadId = null,
@@ -31,7 +31,7 @@ internal sealed class OpenAIChatCompletionsAgentClient(HttpClient httpClient) :
         var openAiClient = new ChatClient(model: "myModel!", credential: new ApiKeyCredential("dummy-key"), options: options).AsIChatClient();
         await foreach (var update in openAiClient.GetStreamingResponseAsync(messages, cancellationToken: cancellationToken))
         {
-            yield return new AgentResponseUpdate(update);
+            yield return new AgentRunResponseUpdate(update);
         }
     }
 }

dotnet/samples/AgentWebChat/AgentWebChat.Web/OpenAIResponsesAgentClient.cs

@@ -15,7 +15,7 @@ namespace AgentWebChat.Web;
 /// </summary>
 internal sealed class OpenAIResponsesAgentClient(HttpClient httpClient) : AgentClientBase
 {
-    public override async IAsyncEnumerable<AgentResponseUpdate> RunStreamingAsync(
+    public override async IAsyncEnumerable<AgentRunResponseUpdate> RunStreamingAsync(
         string agentName,
         IList<ChatMessage> messages,
         string? threadId = null,
@@ -35,7 +35,7 @@ internal sealed class OpenAIResponsesAgentClient(HttpClient httpClient) : AgentC
 
         await foreach (var update in openAiClient.GetStreamingResponseAsync(messages, chatOptions, cancellationToken: cancellationToken))
         {
-            yield return new AgentResponseUpdate(update);
+            yield return new AgentRunResponseUpdate(update);
         }
     }
 }

dotnet/samples/AzureFunctions/01_SingleAgent/Program.cs

@@ -25,7 +25,7 @@ AzureOpenAIClient client = !string.IsNullOrEmpty(azureOpenAiKey)
 const string JokerName = "Joker";
 const string JokerInstructions = "You are good at telling jokes.";
 
-AIAgent agent = client.GetChatClient(deploymentName).AsAIAgent(JokerInstructions, JokerName);
+AIAgent agent = client.GetChatClient(deploymentName).CreateAIAgent(JokerInstructions, JokerName);
 
 // Configure the function app to host the AI agent.
 // This will automatically generate HTTP API endpoints for the agent.

dotnet/samples/AzureFunctions/02_AgentOrchestration_Chaining/FunctionTriggers.cs

@@ -19,13 +19,13 @@ public static class FunctionTriggers
     public static async Task<string> RunOrchestrationAsync([OrchestrationTrigger] TaskOrchestrationContext context)
     {
         DurableAIAgent writer = context.GetAgent("WriterAgent");
-        AgentThread writerThread = await writer.GetNewThreadAsync();
+        AgentThread writerThread = writer.GetNewThread();
 
-        AgentResponse<TextResponse> initial = await writer.RunAsync<TextResponse>(
+        AgentRunResponse<TextResponse> initial = await writer.RunAsync<TextResponse>(
             message: "Write a concise inspirational sentence about learning.",
             thread: writerThread);
 
-        AgentResponse<TextResponse> refined = await writer.RunAsync<TextResponse>(
+        AgentRunResponse<TextResponse> refined = await writer.RunAsync<TextResponse>(
             message: $"Improve this further while keeping it under 25 words: {initial.Result.Text}",
             thread: writerThread);
 

dotnet/samples/AzureFunctions/02_AgentOrchestration_Chaining/Program.cs

@@ -29,7 +29,7 @@ const string WriterInstructions =
     when given an improved sentence you polish it further.
     """;
 
-AIAgent writerAgent = client.GetChatClient(deploymentName).AsAIAgent(WriterInstructions, WriterName);
+AIAgent writerAgent = client.GetChatClient(deploymentName).CreateAIAgent(WriterInstructions, WriterName);
 
 using IHost app = FunctionsApplication
     .CreateBuilder(args)

dotnet/samples/AzureFunctions/03_AgentOrchestration_Concurrency/FunctionTriggers.cs

@@ -26,9 +26,9 @@ public static class FunctionsTriggers
         DurableAIAgent chemist = context.GetAgent("ChemistAgent");
 
         // Start both agent runs concurrently
-        Task<AgentResponse<TextResponse>> physicistTask = physicist.RunAsync<TextResponse>(prompt);
+        Task<AgentRunResponse<TextResponse>> physicistTask = physicist.RunAsync<TextResponse>(prompt);
 
-        Task<AgentResponse<TextResponse>> chemistTask = chemist.RunAsync<TextResponse>(prompt);
+        Task<AgentRunResponse<TextResponse>> chemistTask = chemist.RunAsync<TextResponse>(prompt);
 
         // Wait for both tasks to complete using Task.WhenAll
         await Task.WhenAll(physicistTask, chemistTask);

dotnet/samples/AzureFunctions/03_AgentOrchestration_Concurrency/Program.cs

@@ -28,8 +28,8 @@ const string PhysicistInstructions = "You are an expert in physics. You answer q
 const string ChemistName = "ChemistAgent";
 const string ChemistInstructions = "You are an expert in chemistry. You answer questions from a chemistry perspective.";
 
-AIAgent physicistAgent = client.GetChatClient(deploymentName).AsAIAgent(PhysicistInstructions, PhysicistName);
-AIAgent chemistAgent = client.GetChatClient(deploymentName).AsAIAgent(ChemistInstructions, ChemistName);
+AIAgent physicistAgent = client.GetChatClient(deploymentName).CreateAIAgent(PhysicistInstructions, PhysicistName);
+AIAgent chemistAgent = client.GetChatClient(deploymentName).CreateAIAgent(ChemistInstructions, ChemistName);
 
 using IHost app = FunctionsApplication
     .CreateBuilder(args)

dotnet/samples/AzureFunctions/04_AgentOrchestration_Conditionals/FunctionTriggers.cs

@@ -21,10 +21,10 @@ public static class FunctionTriggers
 
         // Get the spam detection agent
         DurableAIAgent spamDetectionAgent = context.GetAgent("SpamDetectionAgent");
-        AgentThread spamThread = await spamDetectionAgent.GetNewThreadAsync();
+        AgentThread spamThread = spamDetectionAgent.GetNewThread();
 
         // Step 1: Check if the email is spam
-        AgentResponse<DetectionResult> spamDetectionResponse = await spamDetectionAgent.RunAsync<DetectionResult>(
+        AgentRunResponse<DetectionResult> spamDetectionResponse = await spamDetectionAgent.RunAsync<DetectionResult>(
             message:
                 $"""
                 Analyze this email for spam content and return a JSON response with 'is_spam' (boolean) and 'reason' (string) fields:
@@ -43,9 +43,9 @@ public static class FunctionTriggers
 
         // Generate and send response for legitimate email
         DurableAIAgent emailAssistantAgent = context.GetAgent("EmailAssistantAgent");
-        AgentThread emailThread = await emailAssistantAgent.GetNewThreadAsync();
+        AgentThread emailThread = emailAssistantAgent.GetNewThread();
 
-        AgentResponse<EmailResponse> emailAssistantResponse = await emailAssistantAgent.RunAsync<EmailResponse>(
+        AgentRunResponse<EmailResponse> emailAssistantResponse = await emailAssistantAgent.RunAsync<EmailResponse>(
             message:
                 $"""
                     Draft a professional response to this email. Return a JSON response with a 'response' field containing the reply:

dotnet/samples/AzureFunctions/04_AgentOrchestration_Conditionals/Program.cs

@@ -29,10 +29,10 @@ const string EmailAssistantName = "EmailAssistantAgent";
 const string EmailAssistantInstructions = "You are an email assistant that helps users draft responses to emails with professionalism.";
 
 AIAgent spamDetectionAgent = client.GetChatClient(deploymentName)
-    .AsAIAgent(SpamDetectionInstructions, SpamDetectionName);
+    .CreateAIAgent(SpamDetectionInstructions, SpamDetectionName);
 
 AIAgent emailAssistantAgent = client.GetChatClient(deploymentName)
-    .AsAIAgent(EmailAssistantInstructions, EmailAssistantName);
+    .CreateAIAgent(EmailAssistantInstructions, EmailAssistantName);
 
 using IHost app = FunctionsApplication
     .CreateBuilder(args)

dotnet/samples/AzureFunctions/05_AgentOrchestration_HITL/FunctionTriggers.cs

@@ -24,13 +24,13 @@ public static class FunctionTriggers
 
         // Get the writer agent
         DurableAIAgent writerAgent = context.GetAgent("WriterAgent");
-        AgentThread writerThread = await writerAgent.GetNewThreadAsync();
+        AgentThread writerThread = writerAgent.GetNewThread();
 
         // Set initial status
         context.SetCustomStatus($"Starting content generation for topic: {input.Topic}");
 
         // Step 1: Generate initial content
-        AgentResponse<GeneratedContent> writerResponse = await writerAgent.RunAsync<GeneratedContent>(
+        AgentRunResponse<GeneratedContent> writerResponse = await writerAgent.RunAsync<GeneratedContent>(
             message: $"Write a short article about '{input.Topic}'.",
             thread: writerThread);
         GeneratedContent content = writerResponse.Result;

dotnet/samples/AzureFunctions/05_AgentOrchestration_HITL/Program.cs

@@ -29,7 +29,7 @@ const string WriterInstructions =
     You write engaging, informative, and well-structured content that follows best practices for readability and accuracy.
     """;
 
-AIAgent writerAgent = client.GetChatClient(deploymentName).AsAIAgent(WriterInstructions, WriterName);
+AIAgent writerAgent = client.GetChatClient(deploymentName).CreateAIAgent(WriterInstructions, WriterName);
 
 using IHost app = FunctionsApplication
     .CreateBuilder(args)

dotnet/samples/AzureFunctions/06_LongRunningTools/FunctionTriggers.cs

@@ -20,13 +20,13 @@ public static class FunctionTriggers
 
         // Get the writer agent
         DurableAIAgent writerAgent = context.GetAgent("Writer");
-        AgentThread writerThread = await writerAgent.GetNewThreadAsync();
+        AgentThread writerThread = writerAgent.GetNewThread();
 
         // Set initial status
         context.SetCustomStatus($"Starting content generation for topic: {input.Topic}");
 
         // Step 1: Generate initial content
-        AgentResponse<GeneratedContent> writerResponse = await writerAgent.RunAsync<GeneratedContent>(
+        AgentRunResponse<GeneratedContent> writerResponse = await writerAgent.RunAsync<GeneratedContent>(
             message: $"Write a short article about '{input.Topic}'.",
             thread: writerThread);
         GeneratedContent content = writerResponse.Result;

dotnet/samples/AzureFunctions/06_LongRunningTools/Program.cs

@@ -33,7 +33,7 @@ const string WriterAgentInstructions =
     You write engaging, informative, and well-structured content that follows best practices for readability and accuracy.
     """;
 
-AIAgent writerAgent = client.GetChatClient(deploymentName).AsAIAgent(WriterAgentInstructions, WriterAgentName);
+AIAgent writerAgent = client.GetChatClient(deploymentName).CreateAIAgent(WriterAgentInstructions, WriterAgentName);
 
 // Agent that can start content generation workflows using tools
 const string PublisherAgentName = "Publisher";
@@ -57,7 +57,7 @@ using IHost app = FunctionsApplication
             // Initialize the tools to be used by the agent.
             Tools publisherTools = new(sp.GetRequiredService<ILogger<Tools>>());
 
-            return client.GetChatClient(deploymentName).AsAIAgent(
+            return client.GetChatClient(deploymentName).CreateAIAgent(
                 instructions: PublisherAgentInstructions,
                 name: PublisherAgentName,
                 services: sp,

dotnet/samples/AzureFunctions/07_AgentAsMcpTool/Program.cs

@@ -28,13 +28,13 @@ AzureOpenAIClient client = !string.IsNullOrEmpty(azureOpenAiKey)
     : new AzureOpenAIClient(new Uri(endpoint), new AzureCliCredential());
 
 // Define three AI agents we are going to use in this application.
-AIAgent agent1 = client.GetChatClient(deploymentName).AsAIAgent("You are good at telling jokes.", "Joker");
+AIAgent agent1 = client.GetChatClient(deploymentName).CreateAIAgent("You are good at telling jokes.", "Joker");
 
 AIAgent agent2 = client.GetChatClient(deploymentName)
-    .AsAIAgent("Check stock prices.", "StockAdvisor");
+    .CreateAIAgent("Check stock prices.", "StockAdvisor");
 
 AIAgent agent3 = client.GetChatClient(deploymentName)
-    .AsAIAgent("Recommend plants.", "PlantAdvisor", description: "Get plant recommendations.");
+    .CreateAIAgent("Recommend plants.", "PlantAdvisor", description: "Get plant recommendations.");
 
 using IHost app = FunctionsApplication
     .CreateBuilder(args)

dotnet/samples/AzureFunctions/08_ReliableStreaming/FunctionTriggers.cs

@@ -95,7 +95,7 @@ public sealed class FunctionTriggers
         AIAgent agentProxy = durableClient.AsDurableAgentProxy(context, "TravelPlanner");
 
         // Create a new agent thread
-        AgentThread thread = await agentProxy.GetNewThreadAsync(cancellationToken);
+        AgentThread thread = agentProxy.GetNewThread();
         string agentSessionId = thread.GetService<AgentSessionId>().ToString();
 
         this._logger.LogInformation("Creating new agent session: {AgentSessionId}", agentSessionId);

dotnet/samples/AzureFunctions/08_ReliableStreaming/Program.cs

@@ -70,7 +70,7 @@ FunctionsApplicationBuilder builder = FunctionsApplication
         // Define the Travel Planner agent with tools for weather and events
         options.AddAIAgentFactory(TravelPlannerName, sp =>
         {
-            return client.GetChatClient(deploymentName).AsAIAgent(
+            return client.GetChatClient(deploymentName).CreateAIAgent(
                 instructions: TravelPlannerInstructions,
                 name: TravelPlannerName,
                 services: sp,

dotnet/samples/AzureFunctions/08_ReliableStreaming/README.md

@@ -196,7 +196,7 @@ The `id` field is the Redis stream entry ID - use it as the `cursor` parameter t
 
 2. **Agent invoked**: The durable entity (`AgentEntity`) is signaled to run the travel planner agent. This is fire-and-forget from the HTTP request's perspective.
 
-3. **Responses captured**: As the agent generates responses, `RedisStreamResponseHandler` (implementing `IAgentResponseHandler`) extracts the text from each `AgentResponseUpdate` and publishes it to a Redis Stream keyed by session ID.
+3. **Responses captured**: As the agent generates responses, `RedisStreamResponseHandler` (implementing `IAgentResponseHandler`) extracts the text from each `AgentRunResponseUpdate` and publishes it to a Redis Stream keyed by session ID.
 
 4. **Client polls Redis**: The HTTP response streams events by polling the Redis Stream. For SSE format, each event includes the Redis entry ID as the `id` field.
 

dotnet/samples/AzureFunctions/08_ReliableStreaming/RedisStreamResponseHandler.cs

@@ -29,7 +29,7 @@ public readonly record struct StreamChunk(string EntryId, string? Text, bool IsD
 /// </para>
 /// <para>
 /// Each agent session gets its own Redis Stream, keyed by session ID. The stream entries
-/// contain text chunks extracted from <see cref="AgentResponseUpdate"/> objects.
+/// contain text chunks extracted from <see cref="AgentRunResponseUpdate"/> objects.
 /// </para>
 /// </remarks>
 public sealed class RedisStreamResponseHandler : IAgentResponseHandler
@@ -53,7 +53,7 @@ public sealed class RedisStreamResponseHandler : IAgentResponseHandler
 
     /// <inheritdoc/>
     public async ValueTask OnStreamingResponseUpdateAsync(
-        IAsyncEnumerable<AgentResponseUpdate> messageStream,
+        IAsyncEnumerable<AgentRunResponseUpdate> messageStream,
         CancellationToken cancellationToken)
     {
         // Get the current session ID from the DurableAgentContext
@@ -73,7 +73,7 @@ public sealed class RedisStreamResponseHandler : IAgentResponseHandler
         IDatabase db = this._redis.GetDatabase();
         int sequenceNumber = 0;
 
-        await foreach (AgentResponseUpdate update in messageStream.WithCancellation(cancellationToken))
+        await foreach (AgentRunResponseUpdate update in messageStream.WithCancellation(cancellationToken))
         {
             // Extract just the text content - this avoids serialization round-trip issues
             string text = update.Text;
@@ -112,7 +112,7 @@ public sealed class RedisStreamResponseHandler : IAgentResponseHandler
     }
 
     /// <inheritdoc/>
-    public ValueTask OnAgentResponseAsync(AgentResponse message, CancellationToken cancellationToken)
+    public ValueTask OnAgentResponseAsync(AgentRunResponse message, CancellationToken cancellationToken)
     {
         // This handler is optimized for streaming responses.
         // For non-streaming responses, we don't need to store in Redis since

dotnet/samples/DurableAgents/ConsoleApps/01_SingleAgent/01_SingleAgent.csproj

@@ -1,30 +0,0 @@
-<Project Sdk="Microsoft.NET.Sdk">
-  <PropertyGroup>
-    <TargetFrameworks>net10.0</TargetFrameworks>
-    <OutputType>Exe</OutputType>
-    <ImplicitUsings>enable</ImplicitUsings>
-    <Nullable>enable</Nullable>
-    <AssemblyName>SingleAgent</AssemblyName>
-    <RootNamespace>SingleAgent</RootNamespace>
-  </PropertyGroup>
-
-  <ItemGroup>
-    <PackageReference Include="Azure.AI.OpenAI" />
-    <PackageReference Include="Azure.Identity" />
-    <PackageReference Include="Microsoft.DurableTask.Client.AzureManaged" />
-    <PackageReference Include="Microsoft.DurableTask.Worker.AzureManaged" />
-    <PackageReference Include="Microsoft.Extensions.Hosting" />
-  </ItemGroup>
-
-  <!-- Local projects that should be switched to package references when using the sample outside of this MAF repo -->
-  <!--
-  <ItemGroup>
-    <PackageReference Include="Microsoft.Agents.AI.DurableTask" />
-    <PackageReference Include="Microsoft.Agents.AI.OpenAI" />
-  </ItemGroup>
-  -->
-  <ItemGroup>
-    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.DurableTask\Microsoft.Agents.AI.DurableTask.csproj" />
-    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
-  </ItemGroup>
-</Project>

dotnet/samples/DurableAgents/ConsoleApps/01_SingleAgent/Program.cs

@@ -1,103 +0,0 @@
-// Copyright (c) Microsoft. All rights reserved.
-
-using Azure;
-using Azure.AI.OpenAI;
-using Azure.Identity;
-using Microsoft.Agents.AI;
-using Microsoft.Agents.AI.DurableTask;
-using Microsoft.DurableTask.Client.AzureManaged;
-using Microsoft.DurableTask.Worker.AzureManaged;
-using Microsoft.Extensions.AI;
-using Microsoft.Extensions.DependencyInjection;
-using Microsoft.Extensions.Hosting;
-using Microsoft.Extensions.Logging;
-using OpenAI.Chat;
-
-// Get the Azure OpenAI endpoint and deployment name from environment variables.
-string endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT")
-    ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
-string deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT")
-    ?? throw new InvalidOperationException("AZURE_OPENAI_DEPLOYMENT is not set.");
-
-// Get DTS connection string from environment variable
-string dtsConnectionString = Environment.GetEnvironmentVariable("DURABLE_TASK_SCHEDULER_CONNECTION_STRING")
-    ?? "Endpoint=http://localhost:8080;TaskHub=default;Authentication=None";
-
-// Use Azure Key Credential if provided, otherwise use Azure CLI Credential.
-string? azureOpenAiKey = Environment.GetEnvironmentVariable("AZURE_OPENAI_KEY");
-AzureOpenAIClient client = !string.IsNullOrEmpty(azureOpenAiKey)
-    ? new AzureOpenAIClient(new Uri(endpoint), new AzureKeyCredential(azureOpenAiKey))
-    : new AzureOpenAIClient(new Uri(endpoint), new AzureCliCredential());
-
-// Set up an AI agent following the standard Microsoft Agent Framework pattern.
-const string JokerName = "Joker";
-const string JokerInstructions = "You are good at telling jokes.";
-
-AIAgent agent = client.GetChatClient(deploymentName).AsAIAgent(JokerInstructions, JokerName);
-
-// Configure the console app to host the AI agent.
-IHost host = Host.CreateDefaultBuilder(args)
-    .ConfigureLogging(logging => logging.SetMinimumLevel(LogLevel.Warning))
-    .ConfigureServices(services =>
-    {
-        services.ConfigureDurableAgents(
-            options => options.AddAIAgent(agent, timeToLive: TimeSpan.FromHours(1)),
-            workerBuilder: builder => builder.UseDurableTaskScheduler(dtsConnectionString),
-            clientBuilder: builder => builder.UseDurableTaskScheduler(dtsConnectionString));
-    })
-    .Build();
-
-await host.StartAsync();
-
-// Get the agent proxy from services
-IServiceProvider services = host.Services;
-AIAgent agentProxy = services.GetRequiredKeyedService<AIAgent>(JokerName);
-
-// Console colors for better UX
-Console.ForegroundColor = ConsoleColor.Cyan;
-Console.WriteLine("=== Single Agent Console Sample ===");
-Console.ResetColor();
-Console.WriteLine("Enter a message for the Joker agent (or 'exit' to quit):");
-Console.WriteLine();
-
-// Create a thread for the conversation
-AgentThread thread = await agentProxy.GetNewThreadAsync();
-
-while (true)
-{
-    // Read input from stdin
-    Console.ForegroundColor = ConsoleColor.Yellow;
-    Console.Write("You: ");
-    Console.ResetColor();
-
-    string? input = Console.ReadLine();
-    if (string.IsNullOrWhiteSpace(input) || input.Equals("exit", StringComparison.OrdinalIgnoreCase))
-    {
-        break;
-    }
-
-    // Run the agent
-    Console.ForegroundColor = ConsoleColor.Green;
-    Console.Write("Joker: ");
-    Console.ResetColor();
-
-    try
-    {
-        AgentResponse agentResponse = await agentProxy.RunAsync(
-            message: input,
-            thread: thread,
-            cancellationToken: CancellationToken.None);
-
-        Console.WriteLine(agentResponse.Text);
-        Console.WriteLine();
-    }
-    catch (Exception ex)
-    {
-        Console.ForegroundColor = ConsoleColor.Red;
-        Console.Error.WriteLine($"Error: {ex.Message}");
-        Console.ResetColor();
-        Console.WriteLine();
-    }
-}
-
-await host.StopAsync();

dotnet/samples/DurableAgents/ConsoleApps/01_SingleAgent/README.md

@@ -1,56 +0,0 @@
-# Single Agent Sample
-
-This sample demonstrates how to use the durable agents extension to create a simple console app that hosts a single AI agent and provides interactive conversation via stdin/stdout.
-
-## Key Concepts Demonstrated
-
-- Using the Microsoft Agent Framework to define a simple AI agent with a name and instructions.
-- Registering durable agents with the console app and running them interactively.
-- Conversation management (via threads) for isolated interactions.
-
-## Environment Setup
-
-See the [README.md](../README.md) file in the parent directory for more information on how to configure the environment, including how to install and run common sample dependencies.
-
-## Running the Sample
-
-With the environment setup, you can run the sample:
-
-```bash
-cd dotnet/samples/DurableAgents/ConsoleApps/01_SingleAgent
-dotnet run --framework net10.0
-```
-
-The app will prompt you for input. You can interact with the Joker agent:
-
-```text
-=== Single Agent Console Sample ===
-Enter a message for the Joker agent (or 'exit' to quit):
-
-You: Tell me a joke about a pirate.
-Joker: Why don't pirates ever learn the alphabet? Because they always get stuck at "C"!
-
-You: Now explain the joke.
-Joker: The joke plays on the word "sea" (C), which pirates are famously associated with...
-
-You: exit
-```
-
-## Scriptable Usage
-
-You can also pipe input to the app for scriptable usage:
-
-```bash
-echo "Tell me a joke about a pirate." | dotnet run
-```
-
-The app will read from stdin, process the input, and write the response to stdout.
-
-## Viewing Agent State
-
-You can view the state of the agent in the Durable Task Scheduler dashboard:
-
-1. Open your browser and navigate to `http://localhost:8082`
-2. In the dashboard, you can view the state of the Joker agent, including its conversation history and current state
-
-The agent maintains conversation state across multiple interactions, and you can inspect this state in the dashboard to understand how the durable agents extension manages conversation context.

dotnet/samples/DurableAgents/ConsoleApps/02_AgentOrchestration_Chaining/02_AgentOrchestration_Chaining.csproj

@@ -1,30 +0,0 @@
-<Project Sdk="Microsoft.NET.Sdk">
-  <PropertyGroup>
-    <TargetFrameworks>net10.0</TargetFrameworks>
-    <OutputType>Exe</OutputType>
-    <ImplicitUsings>enable</ImplicitUsings>
-    <Nullable>enable</Nullable>
-    <AssemblyName>AgentOrchestration_Chaining</AssemblyName>
-    <RootNamespace>AgentOrchestration_Chaining</RootNamespace>
-  </PropertyGroup>
-
-  <ItemGroup>
-    <PackageReference Include="Azure.AI.OpenAI" />
-    <PackageReference Include="Azure.Identity" />
-    <PackageReference Include="Microsoft.DurableTask.Client.AzureManaged" />
-    <PackageReference Include="Microsoft.DurableTask.Worker.AzureManaged" />
-    <PackageReference Include="Microsoft.Extensions.Hosting" />
-  </ItemGroup>
-
-  <!-- Local projects that should be switched to package references when using the sample outside of this MAF repo -->
-  <!--
-  <ItemGroup>
-    <PackageReference Include="Microsoft.Agents.AI.DurableTask" />
-    <PackageReference Include="Microsoft.Agents.AI.OpenAI" />
-  </ItemGroup>
-  -->
-  <ItemGroup>
-    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.DurableTask\Microsoft.Agents.AI.DurableTask.csproj" />
-    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
-  </ItemGroup>
-</Project>

dotnet/samples/DurableAgents/ConsoleApps/02_AgentOrchestration_Chaining/Models.cs

@@ -1,6 +0,0 @@
-// Copyright (c) Microsoft. All rights reserved.
-
-namespace AgentOrchestration_Chaining;
-
-// Response model
-public sealed record TextResponse(string Text);

dotnet/samples/DurableAgents/ConsoleApps/02_AgentOrchestration_Chaining/Program.cs

@@ -1,148 +0,0 @@
-// Copyright (c) Microsoft. All rights reserved.
-
-using AgentOrchestration_Chaining;
-using Azure;
-using Azure.AI.OpenAI;
-using Azure.Identity;
-using Microsoft.Agents.AI;
-using Microsoft.Agents.AI.DurableTask;
-using Microsoft.DurableTask;
-using Microsoft.DurableTask.Client;
-using Microsoft.DurableTask.Client.AzureManaged;
-using Microsoft.DurableTask.Worker;
-using Microsoft.DurableTask.Worker.AzureManaged;
-using Microsoft.Extensions.DependencyInjection;
-using Microsoft.Extensions.Hosting;
-using Microsoft.Extensions.Logging;
-using OpenAI.Chat;
-using Environment = System.Environment;
-
-// Get the Azure OpenAI endpoint and deployment name from environment variables.
-string endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT")
-    ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
-string deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT")
-    ?? throw new InvalidOperationException("AZURE_OPENAI_DEPLOYMENT is not set.");
-
-// Get DTS connection string from environment variable
-string dtsConnectionString = Environment.GetEnvironmentVariable("DURABLE_TASK_SCHEDULER_CONNECTION_STRING")
-    ?? "Endpoint=http://localhost:8080;TaskHub=default;Authentication=None";
-
-// Use Azure Key Credential if provided, otherwise use Azure CLI Credential.
-string? azureOpenAiKey = Environment.GetEnvironmentVariable("AZURE_OPENAI_KEY");
-AzureOpenAIClient client = !string.IsNullOrEmpty(azureOpenAiKey)
-    ? new AzureOpenAIClient(new Uri(endpoint), new AzureKeyCredential(azureOpenAiKey))
-    : new AzureOpenAIClient(new Uri(endpoint), new AzureCliCredential());
-
-// Single agent used by the orchestration to demonstrate sequential calls on the same thread.
-const string WriterName = "WriterAgent";
-const string WriterInstructions =
-    """
-    You refine short pieces of text. When given an initial sentence you enhance it;
-    when given an improved sentence you polish it further.
-    """;
-
-AIAgent writerAgent = client.GetChatClient(deploymentName).AsAIAgent(WriterInstructions, WriterName);
-
-// Orchestrator function
-static async Task<string> RunOrchestratorAsync(TaskOrchestrationContext context)
-{
-    DurableAIAgent writer = context.GetAgent("WriterAgent");
-    AgentThread writerThread = await writer.GetNewThreadAsync();
-
-    AgentResponse<TextResponse> initial = await writer.RunAsync<TextResponse>(
-        message: "Write a concise inspirational sentence about learning.",
-        thread: writerThread);
-
-    AgentResponse<TextResponse> refined = await writer.RunAsync<TextResponse>(
-        message: $"Improve this further while keeping it under 25 words: {initial.Result.Text}",
-        thread: writerThread);
-
-    return refined.Result.Text;
-}
-
-// Configure the console app to host the AI agent.
-IHost host = Host.CreateDefaultBuilder(args)
-    .ConfigureLogging(loggingBuilder => loggingBuilder.SetMinimumLevel(LogLevel.Warning))
-    .ConfigureServices(services =>
-    {
-        services.ConfigureDurableAgents(
-            options => options.AddAIAgent(writerAgent),
-            workerBuilder: builder =>
-            {
-                builder.UseDurableTaskScheduler(dtsConnectionString);
-                builder.AddTasks(registry => registry.AddOrchestratorFunc(nameof(RunOrchestratorAsync), RunOrchestratorAsync));
-            },
-            clientBuilder: builder => builder.UseDurableTaskScheduler(dtsConnectionString));
-    })
-    .Build();
-
-await host.StartAsync();
-
-DurableTaskClient durableClient = host.Services.GetRequiredService<DurableTaskClient>();
-
-// Console colors for better UX
-Console.ForegroundColor = ConsoleColor.Cyan;
-Console.WriteLine("=== Single Agent Orchestration Chaining Sample ===");
-Console.ResetColor();
-Console.WriteLine("Starting orchestration...");
-Console.WriteLine();
-
-try
-{
-    // Start the orchestration
-    string instanceId = await durableClient.ScheduleNewOrchestrationInstanceAsync(
-        orchestratorName: nameof(RunOrchestratorAsync));
-
-    Console.ForegroundColor = ConsoleColor.Gray;
-    Console.WriteLine($"Orchestration started with instance ID: {instanceId}");
-    Console.WriteLine("Waiting for completion...");
-    Console.ResetColor();
-
-    // Wait for orchestration to complete
-    OrchestrationMetadata status = await durableClient.WaitForInstanceCompletionAsync(
-        instanceId,
-        getInputsAndOutputs: true,
-        CancellationToken.None);
-
-    Console.WriteLine();
-
-    if (status.RuntimeStatus == OrchestrationRuntimeStatus.Completed)
-    {
-        Console.ForegroundColor = ConsoleColor.Green;
-        Console.WriteLine("✓ Orchestration completed successfully!");
-        Console.ResetColor();
-        Console.WriteLine();
-        Console.ForegroundColor = ConsoleColor.Yellow;
-        Console.Write("Result: ");
-        Console.ResetColor();
-        Console.WriteLine(status.ReadOutputAs<string>());
-    }
-    else if (status.RuntimeStatus == OrchestrationRuntimeStatus.Failed)
-    {
-        Console.ForegroundColor = ConsoleColor.Red;
-        Console.WriteLine("✗ Orchestration failed!");
-        Console.ResetColor();
-        if (status.FailureDetails != null)
-        {
-            Console.WriteLine($"Error: {status.FailureDetails.ErrorMessage}");
-        }
-        Environment.Exit(1);
-    }
-    else
-    {
-        Console.ForegroundColor = ConsoleColor.Yellow;
-        Console.WriteLine($"Orchestration status: {status.RuntimeStatus}");
-        Console.ResetColor();
-    }
-}
-catch (Exception ex)
-{
-    Console.ForegroundColor = ConsoleColor.Red;
-    Console.Error.WriteLine($"Error: {ex.Message}");
-    Console.ResetColor();
-    Environment.Exit(1);
-}
-finally
-{
-    await host.StopAsync();
-}

dotnet/samples/DurableAgents/ConsoleApps/02_AgentOrchestration_Chaining/README.md

@@ -1,53 +0,0 @@
-# Single Agent Orchestration Sample
-
-This sample demonstrates how to use the durable agents extension to create a simple console app that orchestrates sequential calls to a single AI agent using the same conversation thread for context continuity.
-
-## Key Concepts Demonstrated
-
-- Orchestrating multiple interactions with the same agent in a deterministic order
-- Using the same `AgentThread` across multiple calls to maintain conversational context
-- Durable orchestration with automatic checkpointing and resumption from failures
-- Waiting for orchestration completion using `WaitForInstanceCompletionAsync`
-
-## Environment Setup
-
-See the [README.md](../README.md) file in the parent directory for more information on how to configure the environment, including how to install and run common sample dependencies.
-
-## Running the Sample
-
-With the environment setup, you can run the sample:
-
-```bash
-cd dotnet/samples/DurableAgents/ConsoleApps/02_AgentOrchestration_Chaining
-dotnet run --framework net10.0
-```
-
-The app will start the orchestration, wait for it to complete, and display the result:
-
-```text
-=== Single Agent Orchestration Chaining Sample ===
-Starting orchestration...
-
-Orchestration started with instance ID: 86313f1d45fb42eeb50b1852626bf3ff
-Waiting for completion...
-
-✓ Orchestration completed successfully!
-
-Result: Learning serves as the key, opening doors to boundless opportunities and a brighter future.
-```
-
-The orchestration will proceed to run the WriterAgent twice in sequence:
-
-1. First, it writes an inspirational sentence about learning
-2. Then, it refines the initial output using the same conversation thread
-
-## Viewing Orchestration State
-
-You can view the state of the orchestration in the Durable Task Scheduler dashboard:
-
-1. Open your browser and navigate to `http://localhost:8082`
-2. In the dashboard, you can see:
-   - **Orchestrations**: View the orchestration instance, including its runtime status, input, output, and execution history
-   - **Agents**: View the state of the WriterAgent, including conversation history maintained across the orchestration steps
-
-The orchestration instance ID is displayed in the console output. You can use this ID to find the specific orchestration in the dashboard and inspect its execution details, including the sequence of agent calls and their results.

dotnet/samples/DurableAgents/ConsoleApps/03_AgentOrchestration_Concurrency/03_AgentOrchestration_Concurrency.csproj

@@ -1,30 +0,0 @@
-<Project Sdk="Microsoft.NET.Sdk">
-  <PropertyGroup>
-    <TargetFrameworks>net10.0</TargetFrameworks>
-    <OutputType>Exe</OutputType>
-    <ImplicitUsings>enable</ImplicitUsings>
-    <Nullable>enable</Nullable>
-    <AssemblyName>AgentOrchestration_Concurrency</AssemblyName>
-    <RootNamespace>AgentOrchestration_Concurrency</RootNamespace>
-  </PropertyGroup>
-
-  <ItemGroup>
-    <PackageReference Include="Azure.AI.OpenAI" />
-    <PackageReference Include="Azure.Identity" />
-    <PackageReference Include="Microsoft.DurableTask.Client.AzureManaged" />
-    <PackageReference Include="Microsoft.DurableTask.Worker.AzureManaged" />
-    <PackageReference Include="Microsoft.Extensions.Hosting" />
-  </ItemGroup>
-
-  <!-- Local projects that should be switched to package references when using the sample outside of this MAF repo -->
-  <!--
-  <ItemGroup>
-    <PackageReference Include="Microsoft.Agents.AI.DurableTask" />
-    <PackageReference Include="Microsoft.Agents.AI.OpenAI" />
-  </ItemGroup>
-  -->
-  <ItemGroup>
-    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.DurableTask\Microsoft.Agents.AI.DurableTask.csproj" />
-    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
-  </ItemGroup>
-</Project>

dotnet/samples/DurableAgents/ConsoleApps/03_AgentOrchestration_Concurrency/Models.cs

@@ -1,6 +0,0 @@
-// Copyright (c) Microsoft. All rights reserved.
-
-namespace AgentOrchestration_Concurrency;
-
-// Response model
-public sealed record TextResponse(string Text);

dotnet/samples/DurableAgents/ConsoleApps/03_AgentOrchestration_Concurrency/Program.cs

@@ -1,191 +0,0 @@
-// Copyright (c) Microsoft. All rights reserved.
-
-using System.Text.Json;
-using AgentOrchestration_Concurrency;
-using Azure;
-using Azure.AI.OpenAI;
-using Azure.Identity;
-using Microsoft.Agents.AI;
-using Microsoft.Agents.AI.DurableTask;
-using Microsoft.DurableTask;
-using Microsoft.DurableTask.Client;
-using Microsoft.DurableTask.Client.AzureManaged;
-using Microsoft.DurableTask.Worker;
-using Microsoft.DurableTask.Worker.AzureManaged;
-using Microsoft.Extensions.DependencyInjection;
-using Microsoft.Extensions.Hosting;
-using Microsoft.Extensions.Logging;
-using OpenAI.Chat;
-
-// Get the Azure OpenAI endpoint and deployment name from environment variables.
-string endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT")
-    ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
-string deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT")
-    ?? throw new InvalidOperationException("AZURE_OPENAI_DEPLOYMENT is not set.");
-
-// Get DTS connection string from environment variable
-string dtsConnectionString = Environment.GetEnvironmentVariable("DURABLE_TASK_SCHEDULER_CONNECTION_STRING")
-    ?? "Endpoint=http://localhost:8080;TaskHub=default;Authentication=None";
-
-// Use Azure Key Credential if provided, otherwise use Azure CLI Credential.
-string? azureOpenAiKey = Environment.GetEnvironmentVariable("AZURE_OPENAI_KEY");
-AzureOpenAIClient client = !string.IsNullOrEmpty(azureOpenAiKey)
-    ? new AzureOpenAIClient(new Uri(endpoint), new AzureKeyCredential(azureOpenAiKey))
-    : new AzureOpenAIClient(new Uri(endpoint), new AzureCliCredential());
-
-// Two agents used by the orchestration to demonstrate concurrent execution.
-const string PhysicistName = "PhysicistAgent";
-const string PhysicistInstructions = "You are an expert in physics. You answer questions from a physics perspective.";
-
-const string ChemistName = "ChemistAgent";
-const string ChemistInstructions = "You are a middle school chemistry teacher. You answer questions so that middle school students can understand.";
-
-AIAgent physicistAgent = client.GetChatClient(deploymentName).AsAIAgent(PhysicistInstructions, PhysicistName);
-AIAgent chemistAgent = client.GetChatClient(deploymentName).AsAIAgent(ChemistInstructions, ChemistName);
-
-// Orchestrator function
-static async Task<object> RunOrchestratorAsync(TaskOrchestrationContext context, string prompt)
-{
-    // Get both agents
-    DurableAIAgent physicist = context.GetAgent(PhysicistName);
-    DurableAIAgent chemist = context.GetAgent(ChemistName);
-
-    // Start both agent runs concurrently
-    Task<AgentResponse<TextResponse>> physicistTask = physicist.RunAsync<TextResponse>(prompt);
-    Task<AgentResponse<TextResponse>> chemistTask = chemist.RunAsync<TextResponse>(prompt);
-
-    // Wait for both tasks to complete using Task.WhenAll
-    await Task.WhenAll(physicistTask, chemistTask);
-
-    // Get the results
-    TextResponse physicistResponse = (await physicistTask).Result;
-    TextResponse chemistResponse = (await chemistTask).Result;
-
-    // Return the result as a structured, anonymous type
-    return new
-    {
-        physicist = physicistResponse.Text,
-        chemist = chemistResponse.Text,
-    };
-}
-
-// Configure the console app to host the AI agents.
-IHost host = Host.CreateDefaultBuilder(args)
-    .ConfigureLogging(loggingBuilder => loggingBuilder.SetMinimumLevel(LogLevel.Warning))
-    .ConfigureServices(services =>
-    {
-        services.ConfigureDurableAgents(
-            options =>
-            {
-                options
-                    .AddAIAgent(physicistAgent)
-                    .AddAIAgent(chemistAgent);
-            },
-            workerBuilder: builder =>
-            {
-                builder.UseDurableTaskScheduler(dtsConnectionString);
-                builder.AddTasks(
-                    registry => registry.AddOrchestratorFunc<string, object>(nameof(RunOrchestratorAsync), RunOrchestratorAsync));
-            },
-            clientBuilder: builder => builder.UseDurableTaskScheduler(dtsConnectionString));
-    })
-    .Build();
-
-await host.StartAsync();
-
-DurableTaskClient durableTaskClient = host.Services.GetRequiredService<DurableTaskClient>();
-
-// Console colors for better UX
-Console.ForegroundColor = ConsoleColor.Cyan;
-Console.WriteLine("=== Multi-Agent Concurrent Orchestration Sample ===");
-Console.ResetColor();
-Console.WriteLine("Enter a question for the agents:");
-Console.WriteLine();
-
-// Read prompt from stdin
-string? prompt = Console.ReadLine();
-if (string.IsNullOrWhiteSpace(prompt))
-{
-    Console.ForegroundColor = ConsoleColor.Red;
-    Console.Error.WriteLine("Error: Prompt is required.");
-    Console.ResetColor();
-    Environment.Exit(1);
-    return;
-}
-
-Console.WriteLine();
-Console.ForegroundColor = ConsoleColor.Gray;
-Console.WriteLine("Starting orchestration...");
-Console.ResetColor();
-
-try
-{
-    // Start the orchestration
-    string instanceId = await durableTaskClient.ScheduleNewOrchestrationInstanceAsync(
-        orchestratorName: nameof(RunOrchestratorAsync),
-        input: prompt);
-
-    Console.ForegroundColor = ConsoleColor.Gray;
-    Console.WriteLine($"Orchestration started with instance ID: {instanceId}");
-    Console.WriteLine("Waiting for completion...");
-    Console.ResetColor();
-
-    // Wait for orchestration to complete
-    OrchestrationMetadata status = await durableTaskClient.WaitForInstanceCompletionAsync(
-        instanceId,
-        getInputsAndOutputs: true,
-        CancellationToken.None);
-
-    Console.WriteLine();
-
-    if (status.RuntimeStatus == OrchestrationRuntimeStatus.Completed)
-    {
-        Console.ForegroundColor = ConsoleColor.Green;
-        Console.WriteLine("✓ Orchestration completed successfully!");
-        Console.ResetColor();
-        Console.WriteLine();
-
-        // Parse the output
-        using JsonDocument doc = JsonDocument.Parse(status.SerializedOutput!);
-        JsonElement output = doc.RootElement;
-
-        Console.ForegroundColor = ConsoleColor.Yellow;
-        Console.WriteLine("Physicist's response:");
-        Console.ResetColor();
-        Console.WriteLine(output.GetProperty("physicist").GetString());
-        Console.WriteLine();
-
-        Console.ForegroundColor = ConsoleColor.Yellow;
-        Console.WriteLine("Chemist's response:");
-        Console.ResetColor();
-        Console.WriteLine(output.GetProperty("chemist").GetString());
-    }
-    else if (status.RuntimeStatus == OrchestrationRuntimeStatus.Failed)
-    {
-        Console.ForegroundColor = ConsoleColor.Red;
-        Console.WriteLine("✗ Orchestration failed!");
-        Console.ResetColor();
-        if (status.FailureDetails != null)
-        {
-            Console.WriteLine($"Error: {status.FailureDetails.ErrorMessage}");
-        }
-        Environment.Exit(1);
-    }
-    else
-    {
-        Console.ForegroundColor = ConsoleColor.Yellow;
-        Console.WriteLine($"Orchestration status: {status.RuntimeStatus}");
-        Console.ResetColor();
-    }
-}
-catch (Exception ex)
-{
-    Console.ForegroundColor = ConsoleColor.Red;
-    Console.Error.WriteLine($"Error: {ex.Message}");
-    Console.ResetColor();
-    Environment.Exit(1);
-}
-finally
-{
-    await host.StopAsync();
-}

dotnet/samples/DurableAgents/ConsoleApps/03_AgentOrchestration_Concurrency/README.md

@@ -1,68 +0,0 @@
-# Multi-Agent Concurrent Orchestration Sample
-
-This sample demonstrates how to use the durable agents extension to create a console app that orchestrates concurrent execution of multiple AI agents using durable orchestration.
-
-## Key Concepts Demonstrated
-
-- Running multiple agents concurrently in a single orchestration
-- Using `Task.WhenAll` to wait for concurrent agent executions
-- Combining results from multiple agents into a single response
-- Waiting for orchestration completion using `WaitForInstanceCompletionAsync`
-
-## Environment Setup
-
-See the [README.md](../README.md) file in the parent directory for more information on how to configure the environment, including how to install and run common sample dependencies.
-
-## Running the Sample
-
-With the environment setup, you can run the sample:
-
-```bash
-cd dotnet/samples/DurableAgents/ConsoleApps/03_AgentOrchestration_Concurrency
-dotnet run --framework net10.0
-```
-
-The app will prompt you for a question:
-
-```text
-=== Multi-Agent Concurrent Orchestration Sample ===
-Enter a question for the agents:
-
-What is temperature?
-```
-
-The orchestration will run both agents concurrently and display their responses:
-
-```text
-Orchestration started with instance ID: 86313f1d45fb42eeb50b1852626bf3ff
-Waiting for completion...
-
-✓ Orchestration completed successfully!
-
-Physicist's response:
-Temperature is a measure of the average kinetic energy of particles in a system...
-
-Chemist's response:
-From a chemistry perspective, temperature is crucial for chemical reactions...
-```
-
-Both agents run in parallel, and the orchestration waits for both to complete before returning the combined results.
-
-## Viewing Orchestration State
-
-You can view the state of the orchestration in the Durable Task Scheduler dashboard:
-
-1. Open your browser and navigate to `http://localhost:8082`
-2. In the dashboard, you can see:
-   - **Orchestrations**: View the orchestration instance, including its runtime status, input, output, and execution history
-   - **Agents**: View the state of both the PhysicistAgent and ChemistAgent, including their individual conversation histories
-
-The orchestration instance ID is displayed in the console output. You can use this ID to find the specific orchestration in the dashboard and inspect how the concurrent agent executions were coordinated, including the timing of when each agent started and completed.
-
-## Scriptable Usage
-
-You can also pipe input to the app:
-
-```bash
-echo "What is temperature?" | dotnet run
-```

dotnet/samples/DurableAgents/ConsoleApps/04_AgentOrchestration_Conditionals/04_AgentOrchestration_Conditionals.csproj

@@ -1,30 +0,0 @@
-<Project Sdk="Microsoft.NET.Sdk">
-  <PropertyGroup>
-    <TargetFrameworks>net10.0</TargetFrameworks>
-    <OutputType>Exe</OutputType>
-    <ImplicitUsings>enable</ImplicitUsings>
-    <Nullable>enable</Nullable>
-    <AssemblyName>AgentOrchestration_Conditionals</AssemblyName>
-    <RootNamespace>AgentOrchestration_Conditionals</RootNamespace>
-  </PropertyGroup>
-
-  <ItemGroup>
-    <PackageReference Include="Azure.AI.OpenAI" />
-    <PackageReference Include="Azure.Identity" />
-    <PackageReference Include="Microsoft.DurableTask.Client.AzureManaged" />
-    <PackageReference Include="Microsoft.DurableTask.Worker.AzureManaged" />
-    <PackageReference Include="Microsoft.Extensions.Hosting" />
-  </ItemGroup>
-
-  <!-- Local projects that should be switched to package references when using the sample outside of this MAF repo -->
-  <!--
-  <ItemGroup>
-    <PackageReference Include="Microsoft.Agents.AI.DurableTask" />
-    <PackageReference Include="Microsoft.Agents.AI.OpenAI" />
-  </ItemGroup>
-  -->
-  <ItemGroup>
-    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.DurableTask\Microsoft.Agents.AI.DurableTask.csproj" />
-    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
-  </ItemGroup>
-</Project>

dotnet/samples/DurableAgents/ConsoleApps/04_AgentOrchestration_Conditionals/Models.cs

@@ -1,38 +0,0 @@
-// Copyright (c) Microsoft. All rights reserved.
-
-using System.Text.Json.Serialization;
-
-namespace AgentOrchestration_Conditionals;
-
-/// <summary>
-/// Represents an email input for spam detection and response generation.
-/// </summary>
-public sealed class Email
-{
-    [JsonPropertyName("email_id")]
-    public string EmailId { get; set; } = string.Empty;
-
-    [JsonPropertyName("email_content")]
-    public string EmailContent { get; set; } = string.Empty;
-}
-
-/// <summary>
-/// Represents the result of spam detection analysis.
-/// </summary>
-public sealed class DetectionResult
-{
-    [JsonPropertyName("is_spam")]
-    public bool IsSpam { get; set; }
-
-    [JsonPropertyName("reason")]
-    public string Reason { get; set; } = string.Empty;
-}
-
-/// <summary>
-/// Represents a generated email response.
-/// </summary>
-public sealed class EmailResponse
-{
-    [JsonPropertyName("response")]
-    public string Response { get; set; } = string.Empty;
-}

dotnet/samples/DurableAgents/ConsoleApps/04_AgentOrchestration_Conditionals/Program.cs

@@ -1,228 +0,0 @@
-// Copyright (c) Microsoft. All rights reserved.
-
-using AgentOrchestration_Conditionals;
-using Azure;
-using Azure.AI.OpenAI;
-using Azure.Identity;
-using Microsoft.Agents.AI;
-using Microsoft.Agents.AI.DurableTask;
-using Microsoft.DurableTask;
-using Microsoft.DurableTask.Client;
-using Microsoft.DurableTask.Client.AzureManaged;
-using Microsoft.DurableTask.Worker;
-using Microsoft.DurableTask.Worker.AzureManaged;
-using Microsoft.Extensions.DependencyInjection;
-using Microsoft.Extensions.Hosting;
-using Microsoft.Extensions.Logging;
-using OpenAI.Chat;
-
-// Get the Azure OpenAI endpoint and deployment name from environment variables.
-string endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT")
-    ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
-string deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT")
-    ?? throw new InvalidOperationException("AZURE_OPENAI_DEPLOYMENT is not set.");
-
-// Get DTS connection string from environment variable
-string dtsConnectionString = Environment.GetEnvironmentVariable("DURABLE_TASK_SCHEDULER_CONNECTION_STRING")
-    ?? "Endpoint=http://localhost:8080;TaskHub=default;Authentication=None";
-
-// Use Azure Key Credential if provided, otherwise use Azure CLI Credential.
-string? azureOpenAiKey = Environment.GetEnvironmentVariable("AZURE_OPENAI_KEY");
-AzureOpenAIClient client = !string.IsNullOrEmpty(azureOpenAiKey)
-    ? new AzureOpenAIClient(new Uri(endpoint), new AzureKeyCredential(azureOpenAiKey))
-    : new AzureOpenAIClient(new Uri(endpoint), new AzureCliCredential());
-
-// Spam detection agent
-const string SpamDetectionAgentName = "SpamDetectionAgent";
-const string SpamDetectionAgentInstructions =
-    """
-    You are an expert email spam detection system. Analyze emails and determine if they are spam.
-    Return your analysis as JSON with 'is_spam' (boolean) and 'reason' (string) fields.
-    """;
-
-// Email assistant agent
-const string EmailAssistantAgentName = "EmailAssistantAgent";
-const string EmailAssistantAgentInstructions =
-    """
-    You are a professional email assistant. Draft professional, courteous, and helpful email responses.
-    Return your response as JSON with a 'response' field containing the reply.
-    """;
-
-AIAgent spamDetectionAgent = client.GetChatClient(deploymentName).AsAIAgent(SpamDetectionAgentInstructions, SpamDetectionAgentName);
-AIAgent emailAssistantAgent = client.GetChatClient(deploymentName).AsAIAgent(EmailAssistantAgentInstructions, EmailAssistantAgentName);
-
-// Orchestrator function
-static async Task<string> RunOrchestratorAsync(TaskOrchestrationContext context, Email email)
-{
-    // Get the spam detection agent
-    DurableAIAgent spamDetectionAgent = context.GetAgent(SpamDetectionAgentName);
-    AgentThread spamThread = await spamDetectionAgent.GetNewThreadAsync();
-
-    // Step 1: Check if the email is spam
-    AgentResponse<DetectionResult> spamDetectionResponse = await spamDetectionAgent.RunAsync<DetectionResult>(
-        message:
-            $"""
-            Analyze this email for spam content and return a JSON response with 'is_spam' (boolean) and 'reason' (string) fields:
-            Email ID: {email.EmailId}
-            Content: {email.EmailContent}
-            """,
-        thread: spamThread);
-    DetectionResult result = spamDetectionResponse.Result;
-
-    // Step 2: Conditional logic based on spam detection result
-    if (result.IsSpam)
-    {
-        // Handle spam email
-        return await context.CallActivityAsync<string>(nameof(HandleSpamEmail), result.Reason);
-    }
-
-    // Generate and send response for legitimate email
-    DurableAIAgent emailAssistantAgent = context.GetAgent(EmailAssistantAgentName);
-    AgentThread emailThread = await emailAssistantAgent.GetNewThreadAsync();
-
-    AgentResponse<EmailResponse> emailAssistantResponse = await emailAssistantAgent.RunAsync<EmailResponse>(
-        message:
-            $"""
-            Draft a professional response to this email. Return a JSON response with a 'response' field containing the reply:
-            
-            Email ID: {email.EmailId}
-            Content: {email.EmailContent}
-            """,
-        thread: emailThread);
-
-    EmailResponse emailResponse = emailAssistantResponse.Result;
-
-    return await context.CallActivityAsync<string>(nameof(SendEmail), emailResponse.Response);
-}
-
-// Activity functions
-static void HandleSpamEmail(TaskActivityContext context, string reason)
-{
-    Console.WriteLine($"Email marked as spam: {reason}");
-}
-
-static void SendEmail(TaskActivityContext context, string message)
-{
-    Console.WriteLine($"Email sent: {message}");
-}
-
-// Configure the console app to host the AI agents.
-IHost host = Host.CreateDefaultBuilder(args)
-    .ConfigureLogging(loggingBuilder => loggingBuilder.SetMinimumLevel(LogLevel.Warning))
-    .ConfigureServices(services =>
-    {
-        services.ConfigureDurableAgents(
-            options =>
-            {
-                options
-                    .AddAIAgent(spamDetectionAgent)
-                    .AddAIAgent(emailAssistantAgent);
-            },
-            workerBuilder: builder =>
-            {
-                builder.UseDurableTaskScheduler(dtsConnectionString);
-                builder.AddTasks(registry =>
-                {
-                    registry.AddOrchestratorFunc<Email>(nameof(RunOrchestratorAsync), RunOrchestratorAsync);
-                    registry.AddActivityFunc<string>(nameof(HandleSpamEmail), HandleSpamEmail);
-                    registry.AddActivityFunc<string>(nameof(SendEmail), SendEmail);
-                });
-            },
-            clientBuilder: builder => builder.UseDurableTaskScheduler(dtsConnectionString));
-    })
-    .Build();
-
-await host.StartAsync();
-
-DurableTaskClient durableTaskClient = host.Services.GetRequiredService<DurableTaskClient>();
-
-// Console colors for better UX
-Console.ForegroundColor = ConsoleColor.Cyan;
-Console.WriteLine("=== Multi-Agent Conditional Orchestration Sample ===");
-Console.ResetColor();
-Console.WriteLine("Enter email content:");
-Console.WriteLine();
-
-// Read email content from stdin
-string? emailContent = Console.ReadLine();
-if (string.IsNullOrWhiteSpace(emailContent))
-{
-    Console.ForegroundColor = ConsoleColor.Red;
-    Console.Error.WriteLine("Error: Email content is required.");
-    Console.ResetColor();
-    Environment.Exit(1);
-    return;
-}
-
-// Generate email ID automatically
-Email email = new()
-{
-    EmailId = $"email-{Guid.NewGuid():N}",
-    EmailContent = emailContent
-};
-
-Console.WriteLine();
-Console.ForegroundColor = ConsoleColor.Gray;
-Console.WriteLine("Starting orchestration...");
-Console.ResetColor();
-
-try
-{
-    // Start the orchestration
-    string instanceId = await durableTaskClient.ScheduleNewOrchestrationInstanceAsync(
-        orchestratorName: nameof(RunOrchestratorAsync),
-        input: email);
-
-    Console.ForegroundColor = ConsoleColor.Gray;
-    Console.WriteLine($"Orchestration started with instance ID: {instanceId}");
-    Console.WriteLine("Waiting for completion...");
-    Console.ResetColor();
-
-    // Wait for orchestration to complete
-    OrchestrationMetadata status = await durableTaskClient.WaitForInstanceCompletionAsync(
-        instanceId,
-        getInputsAndOutputs: true,
-        CancellationToken.None);
-
-    Console.WriteLine();
-
-    if (status.RuntimeStatus == OrchestrationRuntimeStatus.Completed)
-    {
-        Console.ForegroundColor = ConsoleColor.Green;
-        Console.WriteLine("✓ Orchestration completed successfully!");
-        Console.ResetColor();
-        Console.WriteLine();
-        Console.ForegroundColor = ConsoleColor.Yellow;
-        Console.Write("Result: ");
-        Console.ResetColor();
-        Console.WriteLine(status.ReadOutputAs<string>());
-    }
-    else if (status.RuntimeStatus == OrchestrationRuntimeStatus.Failed)
-    {
-        Console.ForegroundColor = ConsoleColor.Red;
-        Console.WriteLine("✗ Orchestration failed!");
-        Console.ResetColor();
-        if (status.FailureDetails != null)
-        {
-            Console.WriteLine($"Error: {status.FailureDetails.ErrorMessage}");
-        }
-        Environment.Exit(1);
-    }
-    else
-    {
-        Console.ForegroundColor = ConsoleColor.Yellow;
-        Console.WriteLine($"Orchestration status: {status.RuntimeStatus}");
-        Console.ResetColor();
-    }
-}
-catch (Exception ex)
-{
-    Console.ForegroundColor = ConsoleColor.Red;
-    Console.Error.WriteLine($"Error: {ex.Message}");
-    Console.ResetColor();
-    Environment.Exit(1);
-}
-finally
-{
-    await host.StopAsync();
-}

dotnet/samples/DurableAgents/ConsoleApps/04_AgentOrchestration_Conditionals/README.md

@@ -1,95 +0,0 @@
-# Multi-Agent Conditional Orchestration Sample
-
-This sample demonstrates how to use the durable agents extension to create a console app that orchestrates multiple AI agents with conditional logic based on the results of previous agent interactions.
-
-## Key Concepts Demonstrated
-
-- Multi-agent orchestration with conditional branching
-- Using agent responses to determine workflow paths
-- Activity functions for non-agent operations
-- Waiting for orchestration completion using `WaitForInstanceCompletionAsync`
-
-## Environment Setup
-
-See the [README.md](../README.md) file in the parent directory for more information on how to configure the environment, including how to install and run common sample dependencies.
-
-## Running the Sample
-
-With the environment setup, you can run the sample:
-
-```bash
-cd dotnet/samples/DurableAgents/ConsoleApps/04_AgentOrchestration_Conditionals
-dotnet run --framework net10.0
-```
-
-The app will prompt you for email content. You can test both legitimate emails and spam emails:
-
-### Testing with a Legitimate Email
-
-```text
-=== Multi-Agent Conditional Orchestration Sample ===
-Enter email content:
-
-Hi John, I hope you're doing well. I wanted to follow up on our meeting yesterday about the quarterly report. Could you please send me the updated figures by Friday? Thanks!
-```
-
-The orchestration will analyze the email and display the result:
-
-```text
-Orchestration started with instance ID: 86313f1d45fb42eeb50b1852626bf3ff
-Waiting for completion...
-
-✓ Orchestration completed successfully!
-
-Result: Email sent: Thank you for your email. I'll prepare the updated figures...
-```
-
-### Testing with a Spam Email
-
-```text
-=== Multi-Agent Conditional Orchestration Sample ===
-Enter email content:
-
-URGENT! You've won $1,000,000! Click here now to claim your prize! Limited time offer! Don't miss out!
-```
-
-The orchestration will detect it as spam and display:
-
-```text
-Orchestration started with instance ID: 86313f1d45fb42eeb50b1852626bf3ff
-Waiting for completion...
-
-✓ Orchestration completed successfully!
-
-Result: Email marked as spam: Contains suspicious claims about winning money and urgent action requests...
-```
-
-## Scriptable Usage
-
-You can also pipe email content to the app:
-
-```bash
-# Test with a legitimate email
-echo "Hi John, I hope you're doing well..." | dotnet run
-
-# Test with a spam email
-echo "URGENT! You've won $1,000,000! Click here now!" | dotnet run
-```
-
-The orchestration will proceed as follows:
-
-1. The SpamDetectionAgent analyzes the email to determine if it's spam
-2. Based on the result:
-   - If spam: The orchestration calls the `HandleSpamEmail` activity function
-   - If not spam: The EmailAssistantAgent drafts a response, then the `SendEmail` activity function is called
-
-## Viewing Orchestration State
-
-You can view the state of the orchestration in the Durable Task Scheduler dashboard:
-
-1. Open your browser and navigate to `http://localhost:8082`
-2. In the dashboard, you can see:
-   - **Orchestrations**: View the orchestration instance, including its runtime status, input, output, and execution history
-   - **Agents**: View the state of both the SpamDetectionAgent and EmailAssistantAgent
-
-The orchestration instance ID is displayed in the console output. You can use this ID to find the specific orchestration in the dashboard and inspect the conditional branching logic, including which path was taken based on the spam detection result.

dotnet/samples/DurableAgents/ConsoleApps/05_AgentOrchestration_HITL/05_AgentOrchestration_HITL.csproj

@@ -1,30 +0,0 @@
-<Project Sdk="Microsoft.NET.Sdk">
-  <PropertyGroup>
-    <TargetFrameworks>net10.0</TargetFrameworks>
-    <OutputType>Exe</OutputType>
-    <ImplicitUsings>enable</ImplicitUsings>
-    <Nullable>enable</Nullable>
-    <AssemblyName>AgentOrchestration_HITL</AssemblyName>
-    <RootNamespace>AgentOrchestration_HITL</RootNamespace>
-  </PropertyGroup>
-
-  <ItemGroup>
-    <PackageReference Include="Azure.AI.OpenAI" />
-    <PackageReference Include="Azure.Identity" />
-    <PackageReference Include="Microsoft.DurableTask.Client.AzureManaged" />
-    <PackageReference Include="Microsoft.DurableTask.Worker.AzureManaged" />
-    <PackageReference Include="Microsoft.Extensions.Hosting" />
-  </ItemGroup>
-
-  <!-- Local projects that should be switched to package references when using the sample outside of this MAF repo -->
-  <!--
-  <ItemGroup>
-    <PackageReference Include="Microsoft.Agents.AI.DurableTask" />
-    <PackageReference Include="Microsoft.Agents.AI.OpenAI" />
-  </ItemGroup>
-  -->
-  <ItemGroup>
-    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.DurableTask\Microsoft.Agents.AI.DurableTask.csproj" />
-    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
-  </ItemGroup>
-</Project>

dotnet/samples/DurableAgents/ConsoleApps/05_AgentOrchestration_HITL/Models.cs

@@ -1,44 +0,0 @@
-// Copyright (c) Microsoft. All rights reserved.
-
-using System.Text.Json.Serialization;
-
-namespace AgentOrchestration_HITL;
-
-/// <summary>
-/// Represents the input for the Human-in-the-Loop content generation workflow.
-/// </summary>
-public sealed class ContentGenerationInput
-{
-    [JsonPropertyName("topic")]
-    public string Topic { get; set; } = string.Empty;
-
-    [JsonPropertyName("max_review_attempts")]
-    public int MaxReviewAttempts { get; set; } = 3;
-
-    [JsonPropertyName("approval_timeout_hours")]
-    public float ApprovalTimeoutHours { get; set; } = 72;
-}
-
-/// <summary>
-/// Represents the content generated by the writer agent.
-/// </summary>
-public sealed class GeneratedContent
-{
-    [JsonPropertyName("title")]
-    public string Title { get; set; } = string.Empty;
-
-    [JsonPropertyName("content")]
-    public string Content { get; set; } = string.Empty;
-}
-
-/// <summary>
-/// Represents the human approval response.
-/// </summary>
-public sealed class HumanApprovalResponse
-{
-    [JsonPropertyName("approved")]
-    public bool Approved { get; set; }
-
-    [JsonPropertyName("feedback")]
-    public string Feedback { get; set; } = string.Empty;
-}

dotnet/samples/DurableAgents/ConsoleApps/05_AgentOrchestration_HITL/Program.cs

@@ -1,333 +0,0 @@
-// Copyright (c) Microsoft. All rights reserved.
-
-using System.Text.Json;
-using AgentOrchestration_HITL;
-using Azure;
-using Azure.AI.OpenAI;
-using Azure.Identity;
-using Microsoft.Agents.AI;
-using Microsoft.Agents.AI.DurableTask;
-using Microsoft.DurableTask;
-using Microsoft.DurableTask.Client;
-using Microsoft.DurableTask.Client.AzureManaged;
-using Microsoft.DurableTask.Worker;
-using Microsoft.DurableTask.Worker.AzureManaged;
-using Microsoft.Extensions.DependencyInjection;
-using Microsoft.Extensions.Hosting;
-using Microsoft.Extensions.Logging;
-using OpenAI.Chat;
-
-// Get the Azure OpenAI endpoint and deployment name from environment variables.
-string endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT")
-    ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
-string deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT")
-    ?? throw new InvalidOperationException("AZURE_OPENAI_DEPLOYMENT is not set.");
-
-// Get DTS connection string from environment variable
-string dtsConnectionString = Environment.GetEnvironmentVariable("DURABLE_TASK_SCHEDULER_CONNECTION_STRING")
-    ?? "Endpoint=http://localhost:8080;TaskHub=default;Authentication=None";
-
-// Use Azure Key Credential if provided, otherwise use Azure CLI Credential.
-string? azureOpenAiKey = Environment.GetEnvironmentVariable("AZURE_OPENAI_KEY");
-AzureOpenAIClient client = !string.IsNullOrEmpty(azureOpenAiKey)
-    ? new AzureOpenAIClient(new Uri(endpoint), new AzureKeyCredential(azureOpenAiKey))
-    : new AzureOpenAIClient(new Uri(endpoint), new AzureCliCredential());
-
-// Single agent used by the orchestration to demonstrate human-in-the-loop workflow.
-const string WriterName = "WriterAgent";
-const string WriterInstructions =
-    """
-    You are a professional content writer who creates high-quality articles on various topics.
-    You write engaging, informative, and well-structured content that follows best practices for readability and accuracy.
-    """;
-
-AIAgent writerAgent = client.GetChatClient(deploymentName).AsAIAgent(WriterInstructions, WriterName);
-
-// Orchestrator function
-static async Task<object> RunOrchestratorAsync(TaskOrchestrationContext context, ContentGenerationInput input)
-{
-    // Get the writer agent
-    DurableAIAgent writerAgent = context.GetAgent("WriterAgent");
-    AgentThread writerThread = await writerAgent.GetNewThreadAsync();
-
-    // Set initial status
-    context.SetCustomStatus($"Starting content generation for topic: {input.Topic}");
-
-    // Step 1: Generate initial content
-    AgentResponse<GeneratedContent> writerResponse = await writerAgent.RunAsync<GeneratedContent>(
-        message: $"Write a short article about '{input.Topic}' in less than 300 words.",
-        thread: writerThread);
-    GeneratedContent content = writerResponse.Result;
-
-    // Human-in-the-loop iteration - we set a maximum number of attempts to avoid infinite loops
-    int iterationCount = 0;
-    while (iterationCount++ < input.MaxReviewAttempts)
-    {
-        context.SetCustomStatus(
-            $"Requesting human feedback. Iteration #{iterationCount}. Timeout: {input.ApprovalTimeoutHours} hour(s).");
-
-        // Step 2: Notify user to review the content
-        await context.CallActivityAsync(nameof(NotifyUserForApproval), content);
-
-        // Step 3: Wait for human feedback with configurable timeout
-        HumanApprovalResponse humanResponse;
-        try
-        {
-            humanResponse = await context.WaitForExternalEvent<HumanApprovalResponse>(
-                eventName: "HumanApproval",
-                timeout: TimeSpan.FromHours(input.ApprovalTimeoutHours));
-        }
-        catch (OperationCanceledException)
-        {
-            // Timeout occurred - treat as rejection
-            context.SetCustomStatus(
-                $"Human approval timed out after {input.ApprovalTimeoutHours} hour(s). Treating as rejection.");
-            throw new TimeoutException($"Human approval timed out after {input.ApprovalTimeoutHours} hour(s).");
-        }
-
-        if (humanResponse.Approved)
-        {
-            context.SetCustomStatus("Content approved by human reviewer. Publishing content...");
-
-            // Step 4: Publish the approved content
-            await context.CallActivityAsync(nameof(PublishContent), content);
-
-            context.SetCustomStatus($"Content published successfully at {context.CurrentUtcDateTime:s}");
-            return new { content = content.Content };
-        }
-
-        context.SetCustomStatus("Content rejected by human reviewer. Incorporating feedback and regenerating...");
-
-        // Incorporate human feedback and regenerate
-        writerResponse = await writerAgent.RunAsync<GeneratedContent>(
-            message: $"""
-                The content was rejected by a human reviewer. Please rewrite the article incorporating their feedback.
-                
-                Human Feedback: {humanResponse.Feedback}
-                """,
-            thread: writerThread);
-
-        content = writerResponse.Result;
-    }
-
-    // If we reach here, it means we exhausted the maximum number of iterations
-    throw new InvalidOperationException(
-        $"Content could not be approved after {input.MaxReviewAttempts} iterations.");
-}
-
-// Activity functions
-static void NotifyUserForApproval(TaskActivityContext context, GeneratedContent content)
-{
-    // In a real implementation, this would send notifications via email, SMS, etc.
-    Console.WriteLine(
-        $"""
-        NOTIFICATION: Please review the following content for approval:
-        Title: {content.Title}
-        Content: {content.Content}
-        Use the approval endpoint to approve or reject this content.
-        """);
-}
-
-static void PublishContent(TaskActivityContext context, GeneratedContent content)
-{
-    // In a real implementation, this would publish to a CMS, website, etc.
-    Console.WriteLine(
-        $"""
-        PUBLISHING: Content has been published successfully.
-        Title: {content.Title}
-        Content: {content.Content}
-        """);
-}
-
-// Configure the console app to host the AI agent.
-IHost host = Host.CreateDefaultBuilder(args)
-    .ConfigureLogging(loggingBuilder => loggingBuilder.SetMinimumLevel(LogLevel.Warning))
-    .ConfigureServices(services =>
-    {
-        services.ConfigureDurableAgents(
-            options => options.AddAIAgent(writerAgent),
-            workerBuilder: builder =>
-            {
-                builder.UseDurableTaskScheduler(dtsConnectionString);
-                builder.AddTasks(registry =>
-                {
-                    registry.AddOrchestratorFunc<ContentGenerationInput>(nameof(RunOrchestratorAsync), RunOrchestratorAsync);
-                    registry.AddActivityFunc<GeneratedContent>(nameof(NotifyUserForApproval), NotifyUserForApproval);
-                    registry.AddActivityFunc<GeneratedContent>(nameof(PublishContent), PublishContent);
-                });
-            },
-            clientBuilder: builder => builder.UseDurableTaskScheduler(dtsConnectionString));
-    })
-    .Build();
-
-await host.StartAsync();
-
-DurableTaskClient durableTaskClient = host.Services.GetRequiredService<DurableTaskClient>();
-
-// Console colors for better UX
-Console.ForegroundColor = ConsoleColor.Cyan;
-Console.WriteLine("=== Human-in-the-Loop Orchestration Sample ===");
-Console.ResetColor();
-Console.WriteLine("Enter topic for content generation:");
-Console.WriteLine();
-
-// Read topic from stdin
-string? topic = Console.ReadLine();
-if (string.IsNullOrWhiteSpace(topic))
-{
-    Console.ForegroundColor = ConsoleColor.Red;
-    Console.Error.WriteLine("Error: Topic is required.");
-    Console.ResetColor();
-    Environment.Exit(1);
-    return;
-}
-
-// Prompt for optional parameters with defaults
-Console.WriteLine();
-Console.WriteLine("Max review attempts (default: 3):");
-string? maxAttemptsInput = Console.ReadLine();
-int maxReviewAttempts = int.TryParse(maxAttemptsInput, out int maxAttempts) && maxAttempts > 0
-    ? maxAttempts
-    : 3;
-
-Console.WriteLine("Approval timeout in hours (default: 72):");
-string? timeoutInput = Console.ReadLine();
-float approvalTimeoutHours = float.TryParse(timeoutInput, out float timeout) && timeout > 0
-    ? timeout
-    : 72;
-
-ContentGenerationInput input = new()
-{
-    Topic = topic,
-    MaxReviewAttempts = maxReviewAttempts,
-    ApprovalTimeoutHours = approvalTimeoutHours
-};
-
-Console.WriteLine();
-Console.ForegroundColor = ConsoleColor.Gray;
-Console.WriteLine("Starting orchestration...");
-Console.ResetColor();
-
-try
-{
-    // Start the orchestration
-    string instanceId = await durableTaskClient.ScheduleNewOrchestrationInstanceAsync(
-        orchestratorName: nameof(RunOrchestratorAsync),
-        input: input);
-
-    Console.ForegroundColor = ConsoleColor.Gray;
-    Console.WriteLine($"Orchestration started with instance ID: {instanceId}");
-    Console.WriteLine("Waiting for human approval...");
-    Console.ResetColor();
-    Console.WriteLine();
-
-    // Monitor orchestration status and handle approval prompts
-    using CancellationTokenSource cts = new();
-    Task orchestrationTask = Task.Run(async () =>
-    {
-        while (!cts.Token.IsCancellationRequested)
-        {
-            OrchestrationMetadata? status = await durableTaskClient.GetInstanceAsync(
-                instanceId,
-                getInputsAndOutputs: true,
-                cts.Token);
-
-            if (status == null)
-            {
-                await Task.Delay(TimeSpan.FromSeconds(1), cts.Token);
-                continue;
-            }
-
-            // Check if we're waiting for approval
-            if (status.SerializedCustomStatus != null)
-            {
-                string? customStatus = status.ReadCustomStatusAs<string>();
-                if (customStatus?.StartsWith("Requesting human feedback", StringComparison.OrdinalIgnoreCase) == true)
-                {
-                    // Prompt user for approval
-                    Console.ForegroundColor = ConsoleColor.Yellow;
-                    Console.WriteLine("Content is ready for review. Check the logs above for details.");
-                    Console.Write("Approve? (y/n): ");
-                    Console.ResetColor();
-
-                    string? approvalInput = Console.ReadLine();
-                    bool approved = approvalInput?.Trim().Equals("y", StringComparison.OrdinalIgnoreCase) == true;
-
-                    Console.Write("Feedback (optional): ");
-                    string? feedback = Console.ReadLine() ?? "";
-
-                    HumanApprovalResponse approvalResponse = new()
-                    {
-                        Approved = approved,
-                        Feedback = feedback
-                    };
-
-                    await durableTaskClient.RaiseEventAsync(instanceId, "HumanApproval", approvalResponse);
-                }
-            }
-
-            if (status.RuntimeStatus is OrchestrationRuntimeStatus.Completed or OrchestrationRuntimeStatus.Failed or OrchestrationRuntimeStatus.Terminated)
-            {
-                break;
-            }
-
-            await Task.Delay(TimeSpan.FromSeconds(1), cts.Token);
-        }
-    }, cts.Token);
-
-    // Wait for orchestration to complete
-    OrchestrationMetadata finalStatus = await durableTaskClient.WaitForInstanceCompletionAsync(
-        instanceId,
-        getInputsAndOutputs: true,
-        CancellationToken.None);
-
-    cts.Cancel();
-    await orchestrationTask;
-
-    Console.WriteLine();
-
-    if (finalStatus.RuntimeStatus == OrchestrationRuntimeStatus.Completed)
-    {
-        Console.ForegroundColor = ConsoleColor.Green;
-        Console.WriteLine("✓ Orchestration completed successfully!");
-        Console.ResetColor();
-        Console.WriteLine();
-
-        JsonElement output = finalStatus.ReadOutputAs<JsonElement>();
-        if (output.TryGetProperty("content", out JsonElement contentElement))
-        {
-            Console.ForegroundColor = ConsoleColor.Yellow;
-            Console.WriteLine("Published content:");
-            Console.ResetColor();
-            Console.WriteLine(contentElement.GetString());
-        }
-    }
-    else if (finalStatus.RuntimeStatus == OrchestrationRuntimeStatus.Failed)
-    {
-        Console.ForegroundColor = ConsoleColor.Red;
-        Console.WriteLine("✗ Orchestration failed!");
-        Console.ResetColor();
-        if (finalStatus.FailureDetails != null)
-        {
-            Console.WriteLine($"Error: {finalStatus.FailureDetails.ErrorMessage}");
-        }
-        Environment.Exit(1);
-    }
-    else
-    {
-        Console.ForegroundColor = ConsoleColor.Yellow;
-        Console.WriteLine($"Orchestration status: {finalStatus.RuntimeStatus}");
-        Console.ResetColor();
-    }
-}
-catch (Exception ex)
-{
-    Console.ForegroundColor = ConsoleColor.Red;
-    Console.Error.WriteLine($"Error: {ex.Message}");
-    Console.ResetColor();
-    Environment.Exit(1);
-}
-finally
-{
-    await host.StopAsync();
-}

dotnet/samples/DurableAgents/ConsoleApps/05_AgentOrchestration_HITL/README.md

@@ -1,73 +0,0 @@
-# Human-in-the-Loop Orchestration Sample
-
-This sample demonstrates how to use the durable agents extension to create a console app that implements a human-in-the-loop workflow using durable orchestration, including interactive approval prompts.
-
-## Key Concepts Demonstrated
-
-- Human-in-the-loop workflows with durable orchestration
-- External event handling for human approval/rejection
-- Timeout handling for approval requests
-- Iterative content refinement based on human feedback
-
-## Environment Setup
-
-See the [README.md](../README.md) file in the parent directory for more information on how to configure the environment, including how to install and run common sample dependencies.
-
-## Running the Sample
-
-With the environment setup, you can run the sample:
-
-```bash
-cd dotnet/samples/DurableAgents/ConsoleApps/05_AgentOrchestration_HITL
-dotnet run --framework net10.0
-```
-
-The app will prompt you for input:
-
-```text
-=== Human-in-the-Loop Orchestration Sample ===
-Enter topic for content generation:
-
-The Future of Artificial Intelligence
-
-Max review attempts (default: 3):
-3
-Approval timeout in hours (default: 72):
-72
-```
-
-The orchestration will generate content and prompt you for approval:
-
-```text
-Orchestration started with instance ID: 86313f1d45fb42eeb50b1852626bf3ff
-
-=== NOTIFICATION: Content Ready for Review ===
-Title: The Future of Artificial Intelligence
-
-Content:
-[Generated content appears here]
-
-Please review the content above and provide your approval.
-
-Content is ready for review. Check the logs above for details.
-Approve? (y/n): n
-Feedback (optional): Please add more details about the ethical implications.
-```
-
-The orchestration will incorporate your feedback and regenerate the content. Once approved, it will publish and complete.
-
-## Viewing Orchestration State
-
-You can view the state of the orchestration in the Durable Task Scheduler dashboard:
-
-1. Open your browser and navigate to `http://localhost:8082`
-2. In the dashboard, you can see:
-   - **Orchestrations**: View the orchestration instance, including its runtime status, custom status (which shows approval state), input, output, and execution history
-   - **Agents**: View the state of the WriterAgent, including conversation history
-
-The orchestration instance ID is displayed in the console output. You can use this ID to find the specific orchestration in the dashboard and inspect:
-
-- The custom status field, which shows the current state of the approval workflow
-- When the orchestration is waiting for external events
-- The iteration count and feedback history
-- The final published content

dotnet/samples/DurableAgents/ConsoleApps/06_LongRunningTools/06_LongRunningTools.csproj

@@ -1,30 +0,0 @@
-<Project Sdk="Microsoft.NET.Sdk">
-  <PropertyGroup>
-    <TargetFrameworks>net10.0</TargetFrameworks>
-    <OutputType>Exe</OutputType>
-    <ImplicitUsings>enable</ImplicitUsings>
-    <Nullable>enable</Nullable>
-    <AssemblyName>LongRunningTools</AssemblyName>
-    <RootNamespace>LongRunningTools</RootNamespace>
-  </PropertyGroup>
-
-  <ItemGroup>
-    <PackageReference Include="Azure.AI.OpenAI" />
-    <PackageReference Include="Azure.Identity" />
-    <PackageReference Include="Microsoft.DurableTask.Client.AzureManaged" />
-    <PackageReference Include="Microsoft.DurableTask.Worker.AzureManaged" />
-    <PackageReference Include="Microsoft.Extensions.Hosting" />
-  </ItemGroup>
-
-  <!-- Local projects that should be switched to package references when using the sample outside of this MAF repo -->
-  <!--
-  <ItemGroup>
-    <PackageReference Include="Microsoft.Agents.AI.DurableTask" />
-    <PackageReference Include="Microsoft.Agents.AI.OpenAI" />
-  </ItemGroup>
-  -->
-  <ItemGroup>
-    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.DurableTask\Microsoft.Agents.AI.DurableTask.csproj" />
-    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
-  </ItemGroup>
-</Project>

dotnet/samples/DurableAgents/ConsoleApps/06_LongRunningTools/Models.cs

@@ -1,44 +0,0 @@
-// Copyright (c) Microsoft. All rights reserved.
-
-using System.Text.Json.Serialization;
-
-namespace LongRunningTools;
-
-/// <summary>
-/// Represents the input for the content generation workflow.
-/// </summary>
-public sealed class ContentGenerationInput
-{
-    [JsonPropertyName("topic")]
-    public string Topic { get; set; } = string.Empty;
-
-    [JsonPropertyName("max_review_attempts")]
-    public int MaxReviewAttempts { get; set; } = 3;
-
-    [JsonPropertyName("approval_timeout_hours")]
-    public float ApprovalTimeoutHours { get; set; } = 72;
-}
-
-/// <summary>
-/// Represents the content generated by the writer agent.
-/// </summary>
-public sealed class GeneratedContent
-{
-    [JsonPropertyName("title")]
-    public string Title { get; set; } = string.Empty;
-
-    [JsonPropertyName("content")]
-    public string Content { get; set; } = string.Empty;
-}
-
-/// <summary>
-/// Represents the human feedback response.
-/// </summary>
-public sealed class HumanFeedbackResponse
-{
-    [JsonPropertyName("approved")]
-    public bool Approved { get; set; }
-
-    [JsonPropertyName("feedback")]
-    public string Feedback { get; set; } = string.Empty;
-}

dotnet/samples/DurableAgents/ConsoleApps/06_LongRunningTools/Program.cs

@@ -1,351 +0,0 @@
-// Copyright (c) Microsoft. All rights reserved.
-
-using System.ComponentModel;
-using Azure;
-using Azure.AI.OpenAI;
-using Azure.Identity;
-using LongRunningTools;
-using Microsoft.Agents.AI;
-using Microsoft.Agents.AI.DurableTask;
-using Microsoft.DurableTask;
-using Microsoft.DurableTask.Client;
-using Microsoft.DurableTask.Client.AzureManaged;
-using Microsoft.DurableTask.Worker;
-using Microsoft.DurableTask.Worker.AzureManaged;
-using Microsoft.Extensions.AI;
-using Microsoft.Extensions.DependencyInjection;
-using Microsoft.Extensions.Hosting;
-using Microsoft.Extensions.Logging;
-using OpenAI.Chat;
-
-// Get the Azure OpenAI endpoint and deployment name from environment variables.
-string endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT")
-    ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
-string deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT")
-    ?? throw new InvalidOperationException("AZURE_OPENAI_DEPLOYMENT is not set.");
-
-// Get DTS connection string from environment variable
-string dtsConnectionString = Environment.GetEnvironmentVariable("DURABLE_TASK_SCHEDULER_CONNECTION_STRING")
-    ?? "Endpoint=http://localhost:8080;TaskHub=default;Authentication=None";
-
-// Use Azure Key Credential if provided, otherwise use Azure CLI Credential.
-string? azureOpenAiKey = Environment.GetEnvironmentVariable("AZURE_OPENAI_KEY");
-AzureOpenAIClient client = !string.IsNullOrEmpty(azureOpenAiKey)
-    ? new AzureOpenAIClient(new Uri(endpoint), new AzureKeyCredential(azureOpenAiKey))
-    : new AzureOpenAIClient(new Uri(endpoint), new AzureCliCredential());
-
-// Agent used by the orchestration to write content.
-const string WriterAgentName = "Writer";
-const string WriterAgentInstructions =
-    """
-    You are a professional content writer who creates high-quality articles on various topics.
-    You write engaging, informative, and well-structured content that follows best practices for readability and accuracy.
-    """;
-
-AIAgent writerAgent = client.GetChatClient(deploymentName).AsAIAgent(WriterAgentInstructions, WriterAgentName);
-
-// Agent that can start content generation workflows using tools
-const string PublisherAgentName = "Publisher";
-const string PublisherAgentInstructions =
-    """
-    You are a publishing agent that can manage content generation workflows.
-    You have access to tools to start, monitor, and raise events for content generation workflows.
-    """;
-
-const string HumanFeedbackEventName = "HumanFeedback";
-
-// Orchestrator function
-static async Task<object> RunOrchestratorAsync(TaskOrchestrationContext context, ContentGenerationInput input)
-{
-    // Get the writer agent
-    DurableAIAgent writerAgent = context.GetAgent(WriterAgentName);
-    AgentThread writerThread = await writerAgent.GetNewThreadAsync();
-
-    // Set initial status
-    context.SetCustomStatus($"Starting content generation for topic: {input.Topic}");
-
-    // Step 1: Generate initial content
-    AgentResponse<GeneratedContent> writerResponse = await writerAgent.RunAsync<GeneratedContent>(
-        message: $"Write a short article about '{input.Topic}'.",
-        thread: writerThread);
-    GeneratedContent content = writerResponse.Result;
-
-    // Human-in-the-loop iteration - we set a maximum number of attempts to avoid infinite loops
-    int iterationCount = 0;
-    while (iterationCount++ < input.MaxReviewAttempts)
-    {
-        context.SetCustomStatus(
-            new
-            {
-                message = "Requesting human feedback.",
-                approvalTimeoutHours = input.ApprovalTimeoutHours,
-                iterationCount,
-                content
-            });
-
-        // Step 2: Notify user to review the content
-        await context.CallActivityAsync(nameof(NotifyUserForApproval), content);
-
-        // Step 3: Wait for human feedback with configurable timeout
-        HumanFeedbackResponse humanResponse;
-        try
-        {
-            humanResponse = await context.WaitForExternalEvent<HumanFeedbackResponse>(
-                eventName: HumanFeedbackEventName,
-                timeout: TimeSpan.FromHours(input.ApprovalTimeoutHours));
-        }
-        catch (OperationCanceledException)
-        {
-            // Timeout occurred - treat as rejection
-            context.SetCustomStatus(
-                new
-                {
-                    message = $"Human approval timed out after {input.ApprovalTimeoutHours} hour(s). Treating as rejection.",
-                    iterationCount,
-                    content
-                });
-            throw new TimeoutException($"Human approval timed out after {input.ApprovalTimeoutHours} hour(s).");
-        }
-
-        if (humanResponse.Approved)
-        {
-            context.SetCustomStatus(new
-            {
-                message = "Content approved by human reviewer. Publishing content...",
-                content
-            });
-
-            // Step 4: Publish the approved content
-            await context.CallActivityAsync(nameof(PublishContent), content);
-
-            context.SetCustomStatus(new
-            {
-                message = $"Content published successfully at {context.CurrentUtcDateTime:s}",
-                humanFeedback = humanResponse,
-                content
-            });
-            return new { content = content.Content };
-        }
-
-        context.SetCustomStatus(new
-        {
-            message = "Content rejected by human reviewer. Incorporating feedback and regenerating...",
-            humanFeedback = humanResponse,
-            content
-        });
-
-        // Incorporate human feedback and regenerate
-        writerResponse = await writerAgent.RunAsync<GeneratedContent>(
-            message: $"""
-                The content was rejected by a human reviewer. Please rewrite the article incorporating their feedback.
-                
-                Human Feedback: {humanResponse.Feedback}
-                """,
-            thread: writerThread);
-
-        content = writerResponse.Result;
-    }
-
-    // If we reach here, it means we exhausted the maximum number of iterations
-    throw new InvalidOperationException(
-        $"Content could not be approved after {input.MaxReviewAttempts} iterations.");
-}
-
-// Activity functions
-static void NotifyUserForApproval(TaskActivityContext context, GeneratedContent content)
-{
-    // In a real implementation, this would send notifications via email, SMS, etc.
-    Console.ForegroundColor = ConsoleColor.DarkMagenta;
-    Console.WriteLine(
-        $"""
-        NOTIFICATION: Please review the following content for approval:
-        Title: {content.Title}
-        Content: {content.Content}
-        """);
-    Console.ResetColor();
-}
-
-static void PublishContent(TaskActivityContext context, GeneratedContent content)
-{
-    // In a real implementation, this would publish to a CMS, website, etc.
-    Console.ForegroundColor = ConsoleColor.DarkMagenta;
-    Console.WriteLine(
-        $"""
-        PUBLISHING: Content has been published successfully.
-        Title: {content.Title}
-        Content: {content.Content}
-        """);
-    Console.ResetColor();
-}
-
-// Tools that demonstrate starting orchestrations from agent tool calls.
-[Description("Starts a content generation workflow and returns the instance ID for tracking.")]
-static string StartContentGenerationWorkflow([Description("The topic for content generation")] string topic)
-{
-    const int MaxReviewAttempts = 3;
-    const float ApprovalTimeoutHours = 72;
-
-    // Schedule the orchestration, which will start running after the tool call completes.
-    string instanceId = DurableAgentContext.Current.ScheduleNewOrchestration(
-        name: nameof(RunOrchestratorAsync),
-        input: new ContentGenerationInput
-        {
-            Topic = topic,
-            MaxReviewAttempts = MaxReviewAttempts,
-            ApprovalTimeoutHours = ApprovalTimeoutHours
-        });
-
-    return $"Workflow started with instance ID: {instanceId}";
-}
-
-[Description("Gets the status of a workflow orchestration and returns a summary of the workflow's current status.")]
-static async Task<object> GetWorkflowStatusAsync(
-    [Description("The instance ID of the workflow to check")] string instanceId,
-    [Description("Whether to include detailed information")] bool includeDetails = true)
-{
-    // Get the current agent context using the thread-static property
-    OrchestrationMetadata? status = await DurableAgentContext.Current.GetOrchestrationStatusAsync(
-        instanceId,
-        includeDetails);
-
-    if (status is null)
-    {
-        return new
-        {
-            instanceId,
-            error = $"Workflow instance '{instanceId}' not found.",
-        };
-    }
-
-    return new
-    {
-        instanceId = status.InstanceId,
-        createdAt = status.CreatedAt,
-        executionStatus = status.RuntimeStatus,
-        workflowStatus = status.SerializedCustomStatus,
-        lastUpdatedAt = status.LastUpdatedAt,
-        failureDetails = status.FailureDetails
-    };
-}
-
-[Description(
-    "Raises a feedback event for the content generation workflow. If approved, the workflow will be published. " +
-    "If rejected, the workflow will generate new content.")]
-static async Task SubmitHumanFeedbackAsync(
-    [Description("The instance ID of the workflow to submit feedback for")] string instanceId,
-    [Description("Feedback to submit")] HumanFeedbackResponse feedback)
-{
-    await DurableAgentContext.Current.RaiseOrchestrationEventAsync(instanceId, HumanFeedbackEventName, feedback);
-}
-
-// Configure the console app to host the AI agents.
-IHost host = Host.CreateDefaultBuilder(args)
-    .ConfigureLogging(loggingBuilder => loggingBuilder.SetMinimumLevel(LogLevel.Warning))
-    .ConfigureServices(services =>
-    {
-        services.ConfigureDurableAgents(
-            options =>
-            {
-                // Add the writer agent used by the orchestration
-                options.AddAIAgent(writerAgent);
-
-                // Define the agent that can start orchestrations from tool calls
-                options.AddAIAgentFactory(PublisherAgentName, sp =>
-                {
-                    return client.GetChatClient(deploymentName).AsAIAgent(
-                        instructions: PublisherAgentInstructions,
-                        name: PublisherAgentName,
-                        services: sp,
-                        tools: [
-                            AIFunctionFactory.Create(StartContentGenerationWorkflow),
-                            AIFunctionFactory.Create(GetWorkflowStatusAsync),
-                            AIFunctionFactory.Create(SubmitHumanFeedbackAsync),
-                        ]);
-                });
-            },
-            workerBuilder: builder =>
-            {
-                builder.UseDurableTaskScheduler(dtsConnectionString);
-                builder.AddTasks(registry =>
-                {
-                    registry.AddOrchestratorFunc<ContentGenerationInput>(nameof(RunOrchestratorAsync), RunOrchestratorAsync);
-                    registry.AddActivityFunc<GeneratedContent>(nameof(NotifyUserForApproval), NotifyUserForApproval);
-                    registry.AddActivityFunc<GeneratedContent>(nameof(PublishContent), PublishContent);
-                });
-            },
-            clientBuilder: builder => builder.UseDurableTaskScheduler(dtsConnectionString));
-    })
-    .Build();
-
-await host.StartAsync();
-
-// Get the agent proxy from services
-IServiceProvider services = host.Services;
-AIAgent? agentProxy = services.GetKeyedService<AIAgent>(PublisherAgentName);
-if (agentProxy == null)
-{
-    Console.ForegroundColor = ConsoleColor.Red;
-    Console.Error.WriteLine("Agent 'Publisher' not found.");
-    Console.ResetColor();
-    Environment.Exit(1);
-    return;
-}
-
-// Console colors for better UX
-Console.ForegroundColor = ConsoleColor.Cyan;
-Console.WriteLine("=== Long Running Tools Sample ===");
-Console.ResetColor();
-Console.WriteLine("Enter a topic for the Publisher agent to write about (or 'exit' to quit):");
-Console.WriteLine();
-
-// Create a thread for the conversation
-AgentThread thread = await agentProxy.GetNewThreadAsync();
-
-using CancellationTokenSource cts = new();
-Console.CancelKeyPress += (sender, e) =>
-{
-    e.Cancel = true;
-    cts.Cancel();
-};
-
-while (!cts.Token.IsCancellationRequested)
-{
-    // Read input from stdin
-    Console.ForegroundColor = ConsoleColor.Yellow;
-    Console.Write("You: ");
-    Console.ResetColor();
-
-    string? input = Console.ReadLine();
-    if (string.IsNullOrWhiteSpace(input) || input.Equals("exit", StringComparison.OrdinalIgnoreCase))
-    {
-        break;
-    }
-
-    // Run the agent
-    Console.ForegroundColor = ConsoleColor.Green;
-    Console.Write("Publisher: ");
-    Console.ResetColor();
-
-    try
-    {
-        AgentResponse agentResponse = await agentProxy.RunAsync(
-            message: input,
-            thread: thread,
-            cancellationToken: cts.Token);
-
-        Console.WriteLine(agentResponse.Text);
-        Console.WriteLine();
-    }
-    catch (Exception ex)
-    {
-        Console.ForegroundColor = ConsoleColor.Red;
-        Console.Error.WriteLine($"Error: {ex.Message}");
-        Console.ResetColor();
-        Console.WriteLine();
-    }
-
-    Console.WriteLine("(Press Enter to prompt the Publisher agent again)");
-    _ = Console.ReadLine();
-}
-
-await host.StopAsync();

dotnet/samples/DurableAgents/ConsoleApps/06_LongRunningTools/README.md

@@ -1,90 +0,0 @@
-# Long Running Tools Sample
-
-This sample demonstrates how to use the durable agents extension to create a console app with agents that have long running tools. This sample builds on the [05_AgentOrchestration_HITL](../05_AgentOrchestration_HITL) sample by adding a publisher agent that can start and manage content generation workflows. A key difference is that the publisher agent knows the IDs of the workflows it starts, so it can check the status of the workflows and approve or reject them without being explicitly given the context (instance IDs, etc).
-
-## Key Concepts Demonstrated
-
-The same key concepts as the [05_AgentOrchestration_HITL](../05_AgentOrchestration_HITL) sample are demonstrated, but with the following additional concepts:
-
-- **Long running tools**: Using `DurableAgentContext.Current` to start orchestrations from tool calls
-- **Multi-agent orchestration**: Agents can start and manage workflows that orchestrate other agents
-- **Human-in-the-loop (with delegation)**: The agent acts as an intermediary between the human and the workflow. The human remains in the loop, but delegates to the agent to start the workflow and approve or reject the content.
-
-## Environment Setup
-
-See the [README.md](../README.md) file in the parent directory for more information on how to configure the environment, including how to install and run common sample dependencies.
-
-## Running the Sample
-
-With the environment setup, you can run the sample:
-
-```bash
-cd dotnet/samples/DurableAgents/ConsoleApps/06_LongRunningTools
-dotnet run --framework net10.0
-```
-
-The app will prompt you for input. You can interact with the Publisher agent:
-
-```text
-=== Long Running Tools Sample ===
-Enter a topic for the Publisher agent to write about (or 'exit' to quit):
-
-You: Start a content generation workflow for the topic 'The Future of Artificial Intelligence'
-Publisher: The content generation workflow for the topic "The Future of Artificial Intelligence" has been successfully started, and the instance ID is **6a04276e8d824d8d941e1dc4142cc254**. If you need any further assistance or updates on the workflow, feel free to ask!
-```
-
-Behind the scenes, the publisher agent will:
-
-1. Start the content generation workflow via a tool call
-2. The workflow will generate initial content using the Writer agent and wait for human approval, which will be visible in the terminal
-
-Once the workflow is waiting for human approval, you can send approval or rejection by prompting the publisher agent accordingly.
-
-> [!NOTE]
-> You must press Enter after each message to continue the conversation. The sample is set up this way because the workflow is running in the background and may write to the console asynchronously.
-
-To tell the agent to rewrite the content with feedback, you can prompt it to reject the content with feedback.
-
-```text
-You: Reject the content with feedback: The article needs more technical depth and better examples.
-Publisher: The content has been successfully rejected with the feedback: "The article needs more technical depth and better examples." The workflow will now generate new content based on this feedback.
-```
-
-Once you're satisfied with the content, you can approve it for publishing.
-
-```text
-You: Approve the content
-Publisher: The content has been successfully approved for publishing. If you need any more assistance or have further requests, feel free to let me know!
-```
-
-Once the workflow has completed, you can get the status by prompting the publisher agent to give you the status.
-
-```text
-You: Get the status of the workflow you previously started
-Publisher: The status of the workflow with instance ID **6a04276e8d824d8d941e1dc4142cc254** is as follows:
-
-- **Execution Status:** Completed
-- **Created At:** December 22, 2025, 23:08:13 UTC
-- **Last Updated At:** December 22, 2025, 23:09:59 UTC
-- **Workflow Status:** 
-  - Message: Content published successfully at December 22, 2025, 23:09:59 UTC
-  - Human Feedback: Approved
-```
-
-## Viewing Agent and Orchestration State
-
-You can view the state of both the agent and the orchestrations it starts in the Durable Task Scheduler dashboard:
-
-1. Open your browser and navigate to `http://localhost:8082`
-2. In the dashboard, you can see:
-   - **Agents**: View the state of the Publisher agent, including its conversation history and tool call history
-   - **Orchestrations**: View the content generation orchestration instances that were started by the agent via tool calls, including their runtime status, custom status, input, output, and execution history
-
-When the publisher agent starts a workflow, the orchestration instance ID is included in the agent's response. You can use this ID to find the specific orchestration in the dashboard and inspect:
-
-- The orchestration's execution progress
-- When it's waiting for human approval (visible in custom status)
-- The content generation workflow state
-- The WriterAgent state within the orchestration
-
-This demonstrates how agents can manage long-running workflows and how you can monitor both the agent's state and the workflows it orchestrates.

dotnet/samples/DurableAgents/ConsoleApps/07_ReliableStreaming/07_ReliableStreaming.csproj

@@ -1,31 +0,0 @@
-<Project Sdk="Microsoft.NET.Sdk">
-  <PropertyGroup>
-    <TargetFrameworks>net10.0</TargetFrameworks>
-    <OutputType>Exe</OutputType>
-    <ImplicitUsings>enable</ImplicitUsings>
-    <Nullable>enable</Nullable>
-    <AssemblyName>ReliableStreaming</AssemblyName>
-    <RootNamespace>ReliableStreaming</RootNamespace>
-  </PropertyGroup>
-
-  <ItemGroup>
-    <PackageReference Include="Azure.AI.OpenAI" />
-    <PackageReference Include="Azure.Identity" />
-    <PackageReference Include="Microsoft.DurableTask.Client.AzureManaged" />
-    <PackageReference Include="Microsoft.DurableTask.Worker.AzureManaged" />
-    <PackageReference Include="Microsoft.Extensions.Hosting" />
-    <PackageReference Include="StackExchange.Redis" />
-  </ItemGroup>
-
-  <!-- Local projects that should be switched to package references when using the sample outside of this MAF repo -->
-  <!--
-  <ItemGroup>
-    <PackageReference Include="Microsoft.Agents.AI.DurableTask" />
-    <PackageReference Include="Microsoft.Agents.AI.OpenAI" />
-  </ItemGroup>
-  -->
-  <ItemGroup>
-    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.DurableTask\Microsoft.Agents.AI.DurableTask.csproj" />
-    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
-  </ItemGroup>
-</Project>

dotnet/samples/DurableAgents/ConsoleApps/07_ReliableStreaming/Program.cs

@@ -1,363 +0,0 @@
-// Copyright (c) Microsoft. All rights reserved.
-
-// This sample demonstrates how to implement reliable streaming for durable agents using Redis Streams.
-// It reads prompts from stdin and streams agent responses to stdout in real-time.
-
-using System.ComponentModel;
-using Azure;
-using Azure.AI.OpenAI;
-using Azure.Identity;
-using Microsoft.Agents.AI;
-using Microsoft.Agents.AI.DurableTask;
-using Microsoft.DurableTask.Client.AzureManaged;
-using Microsoft.DurableTask.Worker.AzureManaged;
-using Microsoft.Extensions.AI;
-using Microsoft.Extensions.DependencyInjection;
-using Microsoft.Extensions.Hosting;
-using Microsoft.Extensions.Logging;
-using OpenAI.Chat;
-using ReliableStreaming;
-using StackExchange.Redis;
-
-// Get the Azure OpenAI endpoint and deployment name from environment variables.
-string endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT")
-    ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
-string deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT")
-    ?? throw new InvalidOperationException("AZURE_OPENAI_DEPLOYMENT is not set.");
-
-// Get Redis connection string from environment variable.
-string redisConnectionString = Environment.GetEnvironmentVariable("REDIS_CONNECTION_STRING")
-    ?? "localhost:6379";
-
-// Get the Redis stream TTL from environment variable (default: 10 minutes).
-int redisStreamTtlMinutes = int.Parse(Environment.GetEnvironmentVariable("REDIS_STREAM_TTL_MINUTES") ?? "10");
-
-// Get DTS connection string from environment variable
-string dtsConnectionString = Environment.GetEnvironmentVariable("DURABLE_TASK_SCHEDULER_CONNECTION_STRING")
-    ?? "Endpoint=http://localhost:8080;TaskHub=default;Authentication=None";
-
-// Use Azure Key Credential if provided, otherwise use Azure CLI Credential.
-string? azureOpenAiKey = Environment.GetEnvironmentVariable("AZURE_OPENAI_KEY");
-AzureOpenAIClient client = !string.IsNullOrEmpty(azureOpenAiKey)
-    ? new AzureOpenAIClient(new Uri(endpoint), new AzureKeyCredential(azureOpenAiKey))
-    : new AzureOpenAIClient(new Uri(endpoint), new AzureCliCredential());
-
-// Travel Planner agent instructions - designed to produce longer responses for demonstrating streaming.
-const string TravelPlannerName = "TravelPlanner";
-const string TravelPlannerInstructions =
-    """
-    You are an expert travel planner who creates detailed, personalized travel itineraries.
-    When asked to plan a trip, you should:
-    1. Create a comprehensive day-by-day itinerary
-    2. Include specific recommendations for activities, restaurants, and attractions
-    3. Provide practical tips for each destination
-    4. Consider weather and local events when making recommendations
-    5. Include estimated times and logistics between activities
-    
-    Always use the available tools to get current weather forecasts and local events
-    for the destination to make your recommendations more relevant and timely.
-    
-    Format your response with clear headings for each day and include emoji icons
-    to make the itinerary easy to scan and visually appealing.
-    """;
-
-// Mock travel tools that return hardcoded data for demonstration purposes.
-[Description("Gets the weather forecast for a destination on a specific date. Use this to provide weather-aware recommendations in the itinerary.")]
-static string GetWeatherForecast(string destination, string date)
-{
-    Dictionary<string, (string condition, int highF, int lowF)> weatherByRegion = new(StringComparer.OrdinalIgnoreCase)
-    {
-        ["Tokyo"] = ("Partly cloudy with a chance of light rain", 58, 45),
-        ["Paris"] = ("Overcast with occasional drizzle", 52, 41),
-        ["New York"] = ("Clear and cold", 42, 28),
-        ["London"] = ("Foggy morning, clearing in afternoon", 48, 38),
-        ["Sydney"] = ("Sunny and warm", 82, 68),
-        ["Rome"] = ("Sunny with light breeze", 62, 48),
-        ["Barcelona"] = ("Partly sunny", 59, 47),
-        ["Amsterdam"] = ("Cloudy with light rain", 46, 38),
-        ["Dubai"] = ("Sunny and hot", 85, 72),
-        ["Singapore"] = ("Tropical thunderstorms in afternoon", 88, 77),
-        ["Bangkok"] = ("Hot and humid, afternoon showers", 91, 78),
-        ["Los Angeles"] = ("Sunny and pleasant", 72, 55),
-        ["San Francisco"] = ("Morning fog, afternoon sun", 62, 52),
-        ["Seattle"] = ("Rainy with breaks", 48, 40),
-        ["Miami"] = ("Warm and sunny", 78, 65),
-        ["Honolulu"] = ("Tropical paradise weather", 82, 72),
-    };
-
-    (string condition, int highF, int lowF) forecast = ("Partly cloudy", 65, 50);
-    foreach (KeyValuePair<string, (string, int, int)> entry in weatherByRegion)
-    {
-        if (destination.Contains(entry.Key, StringComparison.OrdinalIgnoreCase))
-        {
-            forecast = entry.Value;
-            break;
-        }
-    }
-
-    return $"""
-        Weather forecast for {destination} on {date}:
-        Conditions: {forecast.condition}
-        High: {forecast.highF}°F ({(forecast.highF - 32) * 5 / 9}°C)
-        Low: {forecast.lowF}°F ({(forecast.lowF - 32) * 5 / 9}°C)
-        
-        Recommendation: {GetWeatherRecommendation(forecast.condition)}
-        """;
-}
-
-[Description("Gets local events and activities happening at a destination around a specific date. Use this to suggest timely activities and experiences.")]
-static string GetLocalEvents(string destination, string date)
-{
-    Dictionary<string, string[]> eventsByCity = new(StringComparer.OrdinalIgnoreCase)
-    {
-        ["Tokyo"] = [
-            "🎭 Kabuki Theater Performance at Kabukiza Theatre - Traditional Japanese drama",
-            "🌸 Winter Illuminations at Yoyogi Park - Spectacular light displays",
-            "🍜 Ramen Festival at Tokyo Station - Sample ramen from across Japan",
-            "🎮 Gaming Expo at Tokyo Big Sight - Latest video games and technology",
-        ],
-        ["Paris"] = [
-            "🎨 Impressionist Exhibition at Musée d'Orsay - Extended evening hours",
-            "🍷 Wine Tasting Tour in Le Marais - Local sommelier guided",
-            "🎵 Jazz Night at Le Caveau de la Huchette - Historic jazz club",
-            "🥐 French Pastry Workshop - Learn from master pâtissiers",
-        ],
-        ["New York"] = [
-            "🎭 Broadway Show: Hamilton - Limited engagement performances",
-            "🏀 Knicks vs Lakers at Madison Square Garden",
-            "🎨 Modern Art Exhibit at MoMA - New installations",
-            "🍕 Pizza Walking Tour of Brooklyn - Artisan pizzerias",
-        ],
-        ["London"] = [
-            "👑 Royal Collection Exhibition at Buckingham Palace",
-            "🎭 West End Musical: The Phantom of the Opera",
-            "🍺 Craft Beer Festival at Brick Lane",
-            "🎪 Winter Wonderland at Hyde Park - Rides and markets",
-        ],
-        ["Sydney"] = [
-            "🏄 Pro Surfing Competition at Bondi Beach",
-            "🎵 Opera at Sydney Opera House - La Bohème",
-            "🦘 Wildlife Night Safari at Taronga Zoo",
-            "🍽️ Harbor Dinner Cruise with fireworks",
-        ],
-        ["Rome"] = [
-            "🏛️ After-Hours Vatican Tour - Skip the crowds",
-            "🍝 Pasta Making Class in Trastevere",
-            "🎵 Classical Concert at Borghese Gallery",
-            "🍷 Wine Tasting in Roman Cellars",
-        ],
-    };
-
-    string[] events = [
-        "🎭 Local theater performance",
-        "🍽️ Food and wine festival",
-        "🎨 Art gallery opening",
-        "🎵 Live music at local venues",
-    ];
-
-    foreach (KeyValuePair<string, string[]> entry in eventsByCity)
-    {
-        if (destination.Contains(entry.Key, StringComparison.OrdinalIgnoreCase))
-        {
-            events = entry.Value;
-            break;
-        }
-    }
-
-    string eventList = string.Join("\n• ", events);
-    return $"""
-        Local events in {destination} around {date}:
-        
-        • {eventList}
-        
-        💡 Tip: Book popular events in advance as they may sell out quickly!
-        """;
-}
-
-static string GetWeatherRecommendation(string condition)
-{
-    return condition switch
-    {
-        string c when c.Contains("rain", StringComparison.OrdinalIgnoreCase) || c.Contains("drizzle", StringComparison.OrdinalIgnoreCase) =>
-            "Bring an umbrella and waterproof jacket. Consider indoor activities for backup.",
-        string c when c.Contains("fog", StringComparison.OrdinalIgnoreCase) =>
-            "Morning visibility may be limited. Plan outdoor sightseeing for afternoon.",
-        string c when c.Contains("cold", StringComparison.OrdinalIgnoreCase) =>
-            "Layer up with warm clothing. Hot drinks and cozy cafés recommended.",
-        string c when c.Contains("hot", StringComparison.OrdinalIgnoreCase) || c.Contains("warm", StringComparison.OrdinalIgnoreCase) =>
-            "Stay hydrated and use sunscreen. Plan strenuous activities for cooler morning hours.",
-        string c when c.Contains("thunder", StringComparison.OrdinalIgnoreCase) || c.Contains("storm", StringComparison.OrdinalIgnoreCase) =>
-            "Keep an eye on weather updates. Have indoor alternatives ready.",
-        _ => "Pleasant conditions expected. Great day for outdoor exploration!"
-    };
-}
-
-// Configure the console app to host the AI agent.
-IHost host = Host.CreateDefaultBuilder(args)
-    .ConfigureLogging(loggingBuilder => loggingBuilder.SetMinimumLevel(LogLevel.Warning))
-    .ConfigureServices(services =>
-    {
-        services.ConfigureDurableAgents(
-            options =>
-            {
-                // Define the Travel Planner agent with tools for weather and events
-                options.AddAIAgentFactory(TravelPlannerName, sp =>
-                {
-                    return client.GetChatClient(deploymentName).AsAIAgent(
-                        instructions: TravelPlannerInstructions,
-                        name: TravelPlannerName,
-                        services: sp,
-                        tools: [
-                            AIFunctionFactory.Create(GetWeatherForecast),
-                            AIFunctionFactory.Create(GetLocalEvents),
-                        ]);
-                });
-            },
-            workerBuilder: builder => builder.UseDurableTaskScheduler(dtsConnectionString),
-            clientBuilder: builder => builder.UseDurableTaskScheduler(dtsConnectionString));
-
-        // Register Redis connection as a singleton
-        services.AddSingleton<IConnectionMultiplexer>(_ =>
-            ConnectionMultiplexer.Connect(redisConnectionString));
-
-        // Register the Redis stream response handler - this captures agent responses
-        // and publishes them to Redis Streams for reliable delivery.
-        services.AddSingleton(sp =>
-            new RedisStreamResponseHandler(
-                sp.GetRequiredService<IConnectionMultiplexer>(),
-                TimeSpan.FromMinutes(redisStreamTtlMinutes)));
-        services.AddSingleton<IAgentResponseHandler>(sp =>
-            sp.GetRequiredService<RedisStreamResponseHandler>());
-    })
-    .Build();
-
-await host.StartAsync();
-
-// Get the agent proxy from services
-IServiceProvider services = host.Services;
-AIAgent? agentProxy = services.GetKeyedService<AIAgent>(TravelPlannerName);
-RedisStreamResponseHandler streamHandler = services.GetRequiredService<RedisStreamResponseHandler>();
-
-if (agentProxy == null)
-{
-    Console.ForegroundColor = ConsoleColor.Red;
-    Console.Error.WriteLine($"Agent '{TravelPlannerName}' not found.");
-    Console.ResetColor();
-    Environment.Exit(1);
-    return;
-}
-
-// Console colors for better UX
-Console.ForegroundColor = ConsoleColor.Cyan;
-Console.WriteLine("=== Reliable Streaming Sample ===");
-Console.ResetColor();
-Console.WriteLine("Enter a travel planning request (or 'exit' to quit):");
-Console.WriteLine();
-
-string? lastCursor = null;
-
-async Task ReadStreamTask(string conversationId, string? cursor, CancellationToken cancellationToken)
-{
-    // Initialize lastCursor to the starting cursor position
-    // This ensures we have a valid cursor even if cancellation happens before any chunks are processed
-    lastCursor = cursor;
-
-    await foreach (StreamChunk chunk in streamHandler.ReadStreamAsync(conversationId, cursor, cancellationToken))
-    {
-        if (chunk.Error != null)
-        {
-            Console.ForegroundColor = ConsoleColor.Red;
-            Console.Error.WriteLine($"\n[Error: {chunk.Error}]");
-            Console.ResetColor();
-            break;
-        }
-
-        if (chunk.IsDone)
-        {
-            Console.WriteLine();
-            Console.WriteLine();
-            break;
-        }
-
-        if (chunk.Text != null)
-        {
-            Console.Write(chunk.Text);
-        }
-
-        // Always update lastCursor to track the latest entry ID, even if text is null
-        // This ensures we can resume from the correct position after interruption
-        if (!string.IsNullOrEmpty(chunk.EntryId))
-        {
-            lastCursor = chunk.EntryId;
-        }
-    }
-}
-
-// New conversation: prompt from stdin
-Console.ForegroundColor = ConsoleColor.Yellow;
-Console.Write("You: ");
-Console.ResetColor();
-
-string? prompt = Console.ReadLine();
-if (string.IsNullOrWhiteSpace(prompt) || prompt.Equals("exit", StringComparison.OrdinalIgnoreCase))
-{
-    return;
-}
-
-// Create a new agent thread
-AgentThread thread = await agentProxy.GetNewThreadAsync();
-AgentSessionId sessionId = thread.GetService<AgentSessionId>();
-string conversationId = sessionId.ToString();
-
-Console.ForegroundColor = ConsoleColor.Green;
-Console.WriteLine($"Conversation ID: {conversationId}");
-Console.WriteLine("Press [Enter] to interrupt the stream.");
-Console.ResetColor();
-
-// Run the agent in the background
-DurableAgentRunOptions options = new() { IsFireAndForget = true };
-await agentProxy.RunAsync(prompt, thread, options, CancellationToken.None);
-
-bool streamCompleted = false;
-while (!streamCompleted)
-{
-    // On a key press, cancel the cancellation token to stop the stream
-    using CancellationTokenSource userCancellationSource = new();
-    _ = Task.Run(() =>
-    {
-        _ = Console.ReadLine();
-        userCancellationSource.Cancel();
-    });
-
-    try
-    {
-        // Start reading the stream and wait for it to complete
-        await ReadStreamTask(conversationId, lastCursor, userCancellationSource.Token);
-        streamCompleted = true;
-    }
-    catch (OperationCanceledException)
-    {
-        Console.ForegroundColor = ConsoleColor.Yellow;
-        Console.WriteLine("Stream cancelled. Press [Enter] to reconnect and resume the stream from the last cursor.");
-        // Ensure lastCursor is set - if it's still null, we at least have the starting cursor
-        string cursorValue = lastCursor ?? "(n/a)";
-        Console.WriteLine($"Last cursor: {cursorValue}");
-        Console.ResetColor();
-        // Explicitly flush to ensure the message is written immediately
-        Console.Out.Flush();
-    }
-
-    if (!streamCompleted)
-    {
-        Console.ReadLine();
-        Console.ForegroundColor = ConsoleColor.Green;
-        Console.WriteLine($"Resuming conversation: {conversationId} from cursor: {lastCursor ?? "(beginning)"}");
-        Console.ResetColor();
-    }
-}
-
-Console.ForegroundColor = ConsoleColor.Green;
-Console.WriteLine("Conversation completed.");
-Console.ResetColor();
-
-await host.StopAsync();

dotnet/samples/DurableAgents/ConsoleApps/07_ReliableStreaming/README.md

@@ -1,181 +0,0 @@
-# Reliable Streaming with Redis
-
-This sample demonstrates how to implement reliable streaming for durable agents using Redis Streams as a message broker. It enables clients to disconnect and reconnect to ongoing agent responses without losing messages, inspired by [OpenAI's background mode](https://platform.openai.com/docs/guides/background) for the Responses API.
-
-## Key Concepts Demonstrated
-
-- **Reliable message delivery**: Agent responses are persisted to Redis Streams, allowing clients to resume from any point
-- **Real-time streaming**: Chunks are printed to stdout as they arrive (like `tail -f`)
-- **Cursor-based resumption**: Each chunk includes an entry ID that can be used to resume the stream
-- **Fire-and-forget agent invocation**: The agent runs in the background while the client streams from Redis
-
-## Environment Setup
-
-See the [README.md](../README.md) file in the parent directory for more information on how to configure the environment, including how to install and run common sample dependencies.
-
-### Additional Requirements: Redis
-
-This sample requires a Redis instance. Start a local Redis instance using Docker:
-
-```bash
-docker run -d --name redis -p 6379:6379 redis:latest
-```
-
-To verify Redis is running:
-
-```bash
-docker ps | grep redis
-```
-
-## Running the Sample
-
-With the environment setup, you can run the sample:
-
-```bash
-cd dotnet/samples/DurableAgents/ConsoleApps/07_ReliableStreaming
-dotnet run --framework net10.0
-```
-
-The app will prompt you for a travel planning request:
-
-```text
-=== Reliable Streaming Sample ===
-Enter a travel planning request (or 'exit' to quit):
-
-You: Plan a 7-day trip to Tokyo, Japan for next month. Include daily activities, restaurant recommendations, and tips for getting around.
-```
-
-The agent's response will stream to your console in real-time as chunks arrive from Redis:
-
-```text
-Starting new conversation: @dafx-travelplanner@a1b2c3d4e5f67890abcdef1234567890
-Press [Enter] to interrupt the stream.
-
-TravelPlanner: # 7-Day Tokyo Adventure
-
-## Day 1: Arrival and Exploration
-...
-```
-
-### Demonstrating Stream Interruption and Resumption
-
-This is the key feature of reliable streaming. Follow these steps to see it in action:
-
-1. **Start a stream**: Run the app and enter a travel planning request
-2. **Note the conversation ID**: The conversation ID is displayed at the start of the stream (e.g., `Starting new conversation: @dafx-travelplanner@a1b2c3d4e5f67890abcdef1234567890`)
-3. **Interrupt the stream**: While the agent is still generating text, press **`Enter`** to interrupt. The agent continues running in the background - your messages are being saved to Redis.
-4. **Resume the stream**: Press **`Enter`** again to reconnect and resume the stream from the last cursor position. The app will automatically resume from where it left off.
-
-```text
-Starting new conversation: @dafx-travelplanner@a1b2c3d4e5f67890abcdef1234567890
-Press [Enter] to interrupt the stream.
-
-TravelPlanner: # 7-Day Tokyo Adventure
-
-## Day 1: Arrival and Exploration
-[Streaming content...]
-
-[Press Enter to interrupt]
-Stream cancelled. Press [Enter] to reconnect and resume the stream from the last cursor.
-Last cursor: 1734567890123-0
-
-[Press Enter to resume]
-Resuming conversation: @dafx-travelplanner@a1b2c3d4e5f67890abcdef1234567890 from cursor: 1734567890123-0
-
-[Stream continues from where it left off...]
-```
-
-## Viewing Agent State
-
-You can view the state of the agent in the Durable Task Scheduler dashboard:
-
-1. Open your browser and navigate to `http://localhost:8082`
-2. In the dashboard, you can see:
-   - **Agents**: View the state of the TravelPlanner agent, including conversation history and current state
-   - **Orchestrations**: View any orchestrations that may have been triggered by the agent
-
-The conversation ID displayed in the console output (shown as "Starting new conversation: {conversationId}") corresponds to the agent's conversation thread. You can use this to identify the agent in the dashboard and inspect:
-
-- The agent's conversation state
-- Tool calls made by the agent (weather and events lookups)
-- The streaming response state
-
-Note that while the console app streams responses from Redis, the agent state in DTS shows the underlying durable agent execution, including all tool calls and conversation context.
-
-## Architecture Overview
-
-```text
-┌─────────────┐      stdin (prompt)     ┌─────────────────────┐
-│   Client    │  ─────────────────────► │  Console App        │
-│  (stdin)    │                         │  (Program.cs)       │
-└─────────────┘                         └──────────────┬──────┘
-       ▲                                               │
-       │ stdout (chunks)                    Signal Entity
-       │                                               │
-       │                                               ▼
-       │                                    ┌─────────────────────┐
-       │                                    │   AgentEntity       │
-       │                                    │   (Durable Entity)  │
-       │                                    └──────────┬──────────┘
-       │                                               │
-       │                                    IAgentResponseHandler
-       │                                               │
-       │                                               ▼
-       │                                    ┌─────────────────────┐
-       │                                    │ RedisStreamResponse │
-       │                                    │      Handler        │
-       │                                    └──────────┬──────────┘
-       │                                               │
-       │                                     XADD (write)
-       │                                               │
-       │                                               ▼
-       │                                    ┌─────────────────────┐
-       └─────────── XREAD (poll) ────────── │   Redis Streams     │
-                                            │  (Durable Log)      │
-                                            └─────────────────────┘
-```
-
-### Data Flow
-
-1. **Client sends prompt**: The console app reads the prompt from stdin and generates a new agent thread.
-
-2. **Agent invoked**: The durable agent is signaled to run the travel planner agent. This is fire-and-forget from the console app's perspective.
-
-3. **Responses captured**: As the agent generates responses, the `RedisStreamResponseHandler` (implementing `IAgentResponseHandler`) extracts the text from each `AgentRunResponseUpdate` and publishes it to a Redis Stream keyed by the agent session's conversation ID.
-
-4. **Client polls Redis**: The console app streams events by polling the Redis Stream and printing chunks to stdout as they arrive.
-
-5. **Resumption**: If the client interrupts the stream (e.g., by pressing Enter in the sample), it can resume from the last cursor position by providing the conversation ID and cursor to the call to resume the stream.
-
-## Message Delivery Guarantees
-
-This sample provides **at-least-once delivery** with the following characteristics:
-
-- **Durability**: Messages are persisted to Redis Streams with configurable TTL (default: 10 minutes).
-- **Ordering**: Messages are delivered in order within a session.
-- **Real-time**: Chunks are printed as soon as they arrive from Redis.
-
-### Important Considerations
-
-- **No exactly-once delivery**: If a client disconnects exactly when receiving a message, it may receive that message again upon resumption. Clients should handle duplicate messages idempotently.
-- **TTL expiration**: Streams expire after the configured TTL. Clients cannot resume streams that have expired.
-- **Redis guarantees**: Redis streams are backed by Redis persistence mechanisms (RDB/AOF). Ensure your Redis instance is configured for durability as needed.
-
-## Configuration
-
-| Environment Variable | Description | Default |
-|---------------------|-------------|---------|
-| `REDIS_CONNECTION_STRING` | Redis connection string | `localhost:6379` |
-| `REDIS_STREAM_TTL_MINUTES` | How long streams are retained after last write | `10` |
-| `AZURE_OPENAI_ENDPOINT` | Azure OpenAI endpoint URL | (required) |
-| `AZURE_OPENAI_DEPLOYMENT` | Azure OpenAI deployment name | (required) |
-| `AZURE_OPENAI_KEY` | API key (optional, uses Azure CLI auth if not set) | (optional) |
-
-## Cleanup
-
-To stop and remove the Redis Docker containers:
-
-```bash
-docker stop redis
-docker rm redis
-```

dotnet/samples/DurableAgents/ConsoleApps/07_ReliableStreaming/RedisStreamResponseHandler.cs

@@ -1,216 +0,0 @@
-// Copyright (c) Microsoft. All rights reserved.
-
-using System.Runtime.CompilerServices;
-using Microsoft.Agents.AI;
-using Microsoft.Agents.AI.DurableTask;
-using StackExchange.Redis;
-
-namespace ReliableStreaming;
-
-/// <summary>
-/// Represents a chunk of data read from a Redis stream.
-/// </summary>
-/// <param name="EntryId">The Redis stream entry ID (can be used as a cursor for resumption).</param>
-/// <param name="Text">The text content of the chunk, or null if this is a completion/error marker.</param>
-/// <param name="IsDone">True if this chunk marks the end of the stream.</param>
-/// <param name="Error">An error message if something went wrong, or null otherwise.</param>
-public readonly record struct StreamChunk(string EntryId, string? Text, bool IsDone, string? Error);
-
-/// <summary>
-/// An implementation of <see cref="IAgentResponseHandler"/> that publishes agent response updates
-/// to Redis Streams for reliable delivery. This enables clients to disconnect and reconnect
-/// to ongoing agent responses without losing messages.
-/// </summary>
-/// <remarks>
-/// <para>
-/// Redis Streams provide a durable, append-only log that supports consumer groups and message
-/// acknowledgment. This implementation uses auto-generated IDs (which are timestamp-based)
-/// as sequence numbers, allowing clients to resume from any point in the stream.
-/// </para>
-/// <para>
-/// Each agent session gets its own Redis Stream, keyed by session ID. The stream entries
-/// contain text chunks extracted from <see cref="AgentResponseUpdate"/> objects.
-/// </para>
-/// </remarks>
-public sealed class RedisStreamResponseHandler : IAgentResponseHandler
-{
-    private const int MaxEmptyReads = 300; // 5 minutes at 1 second intervals
-    private const int PollIntervalMs = 1000;
-
-    private readonly IConnectionMultiplexer _redis;
-    private readonly TimeSpan _streamTtl;
-
-    /// <summary>
-    /// Initializes a new instance of the <see cref="RedisStreamResponseHandler" /> class.
-    /// </summary>
-    /// <param name="redis">The Redis connection multiplexer.</param>
-    /// <param name="streamTtl">The time-to-live for stream entries. Streams will expire after this duration of inactivity.</param>
-    public RedisStreamResponseHandler(IConnectionMultiplexer redis, TimeSpan streamTtl)
-    {
-        this._redis = redis;
-        this._streamTtl = streamTtl;
-    }
-
-    /// <inheritdoc/>
-    public async ValueTask OnStreamingResponseUpdateAsync(
-        IAsyncEnumerable<AgentResponseUpdate> messageStream,
-        CancellationToken cancellationToken)
-    {
-        // Get the current session ID from the DurableAgentContext
-        // This is set by the AgentEntity before invoking the response handler
-        DurableAgentContext context = DurableAgentContext.Current
-            ?? throw new InvalidOperationException("DurableAgentContext.Current is not set. This handler must be used within a durable agent context.");
-
-        // Get conversation ID from the current thread context, which is only available in the context of
-        // a durable agent execution.
-        string conversationId = context.CurrentThread.GetService<AgentSessionId>().ToString();
-        if (string.IsNullOrEmpty(conversationId))
-        {
-            throw new InvalidOperationException("Unable to determine conversation ID from the current thread.");
-        }
-
-        string streamKey = GetStreamKey(conversationId);
-
-        IDatabase db = this._redis.GetDatabase();
-        int sequenceNumber = 0;
-
-        await foreach (AgentResponseUpdate update in messageStream.WithCancellation(cancellationToken))
-        {
-            // Extract just the text content - this avoids serialization round-trip issues
-            string text = update.Text;
-
-            // Only publish non-empty text chunks
-            if (!string.IsNullOrEmpty(text))
-            {
-                // Create the stream entry with the text and metadata
-                NameValueEntry[] entries =
-                [
-                    new NameValueEntry("text", text),
-                    new NameValueEntry("sequence", sequenceNumber++),
-                    new NameValueEntry("timestamp", DateTimeOffset.UtcNow.ToUnixTimeMilliseconds()),
-                ];
-
-                // Add to the Redis Stream with auto-generated ID (timestamp-based)
-                await db.StreamAddAsync(streamKey, entries);
-
-                // Refresh the TTL on each write to keep the stream alive during active streaming
-                await db.KeyExpireAsync(streamKey, this._streamTtl);
-            }
-        }
-
-        // Add a sentinel entry to mark the end of the stream
-        NameValueEntry[] endEntries =
-        [
-            new NameValueEntry("text", ""),
-            new NameValueEntry("sequence", sequenceNumber),
-            new NameValueEntry("timestamp", DateTimeOffset.UtcNow.ToUnixTimeMilliseconds()),
-            new NameValueEntry("done", "true"),
-        ];
-        await db.StreamAddAsync(streamKey, endEntries);
-
-        // Set final TTL - the stream will be cleaned up after this duration
-        await db.KeyExpireAsync(streamKey, this._streamTtl);
-    }
-
-    /// <inheritdoc/>
-    public ValueTask OnAgentResponseAsync(AgentResponse message, CancellationToken cancellationToken)
-    {
-        // This handler is optimized for streaming responses.
-        // For non-streaming responses, we don't need to store in Redis since
-        // the response is returned directly to the caller.
-        return ValueTask.CompletedTask;
-    }
-
-    /// <summary>
-    /// Reads chunks from a Redis stream for the given session, yielding them as they become available.
-    /// </summary>
-    /// <param name="conversationId">The conversation ID to read from.</param>
-    /// <param name="cursor">Optional cursor to resume from. If null, reads from the beginning.</param>
-    /// <param name="cancellationToken">Cancellation token.</param>
-    /// <returns>An async enumerable of stream chunks.</returns>
-    public async IAsyncEnumerable<StreamChunk> ReadStreamAsync(
-        string conversationId,
-        string? cursor,
-        [EnumeratorCancellation] CancellationToken cancellationToken)
-    {
-        string streamKey = GetStreamKey(conversationId);
-
-        IDatabase db = this._redis.GetDatabase();
-        string startId = string.IsNullOrEmpty(cursor) ? "0-0" : cursor;
-
-        int emptyReadCount = 0;
-        bool hasSeenData = false;
-
-        while (!cancellationToken.IsCancellationRequested)
-        {
-            StreamEntry[]? entries = null;
-            string? errorMessage = null;
-
-            try
-            {
-                entries = await db.StreamReadAsync(streamKey, startId, count: 100);
-            }
-            catch (Exception ex)
-            {
-                errorMessage = ex.Message;
-            }
-
-            if (errorMessage != null)
-            {
-                yield return new StreamChunk(startId, null, false, errorMessage);
-                yield break;
-            }
-
-            // entries is guaranteed to be non-null if errorMessage is null
-            if (entries!.Length == 0)
-            {
-                if (!hasSeenData)
-                {
-                    emptyReadCount++;
-                    if (emptyReadCount >= MaxEmptyReads)
-                    {
-                        yield return new StreamChunk(
-                            startId,
-                            null,
-                            false,
-                            $"Stream not found or timed out after {MaxEmptyReads * PollIntervalMs / 1000} seconds");
-                        yield break;
-                    }
-                }
-
-                await Task.Delay(PollIntervalMs, cancellationToken);
-                continue;
-            }
-
-            hasSeenData = true;
-
-            foreach (StreamEntry entry in entries)
-            {
-                startId = entry.Id.ToString();
-                string? text = entry["text"];
-                string? done = entry["done"];
-
-                if (done == "true")
-                {
-                    yield return new StreamChunk(startId, null, true, null);
-                    yield break;
-                }
-
-                if (!string.IsNullOrEmpty(text))
-                {
-                    yield return new StreamChunk(startId, text, false, null);
-                }
-            }
-        }
-
-        // If we exited the loop due to cancellation, throw to signal the caller
-        cancellationToken.ThrowIfCancellationRequested();
-    }
-
-    /// <summary>
-    /// Gets the Redis Stream key for a given conversation ID.
-    /// </summary>
-    /// <param name="conversationId">The conversation ID.</param>
-    /// <returns>The Redis Stream key.</returns>
-    internal static string GetStreamKey(string conversationId) => $"agent-stream:{conversationId}";
-}

dotnet/samples/DurableAgents/ConsoleApps/README.md

@@ -1,109 +0,0 @@
-# Console App Samples
-
-This directory contains samples for console app hosting of durable agents. These samples use standard I/O (stdin/stdout) for interaction, making them both interactive and scriptable.
-
-- **[01_SingleAgent](01_SingleAgent)**: A sample that demonstrates how to host a single conversational agent in a console app and interact with it via stdin/stdout.
-- **[02_AgentOrchestration_Chaining](02_AgentOrchestration_Chaining)**: A sample that demonstrates how to host a single conversational agent in a console app and invoke it using a durable orchestration.
-- **[03_AgentOrchestration_Concurrency](03_AgentOrchestration_Concurrency)**: A sample that demonstrates how to host multiple agents in a console app and run them concurrently using a durable orchestration.
-- **[04_AgentOrchestration_Conditionals](04_AgentOrchestration_Conditionals)**: A sample that demonstrates how to host multiple agents in a console app and run them sequentially using a durable orchestration with conditionals.
-- **[05_AgentOrchestration_HITL](05_AgentOrchestration_HITL)**: A sample that demonstrates how to implement a human-in-the-loop workflow using durable orchestration, including interactive approval prompts.
-- **[06_LongRunningTools](06_LongRunningTools)**: A sample that demonstrates how agents can start and interact with durable orchestrations from tool calls to enable long-running tool scenarios.
-- **[07_ReliableStreaming](07_ReliableStreaming)**: A sample that demonstrates how to implement reliable streaming for durable agents using Redis Streams, enabling clients to disconnect and reconnect without losing messages.
-
-## Running the Samples
-
-These samples are designed to be run locally in a cloned repository.
-
-### Prerequisites
-
-The following prerequisites are required to run the samples:
-
-- [.NET 10.0 SDK or later](https://dotnet.microsoft.com/download/dotnet)
-- [Azure CLI](https://learn.microsoft.com/cli/azure/install-azure-cli) installed and authenticated (`az login`) or an API key for the Azure OpenAI service
-- [Azure OpenAI Service](https://learn.microsoft.com/azure/ai-services/openai/how-to/create-resource) with a deployed model (gpt-4o-mini or better is recommended)
-- [Durable Task Scheduler](https://learn.microsoft.com/azure/azure-functions/durable/durable-task-scheduler/develop-with-durable-task-scheduler) (local emulator or Azure-hosted)
-- [Docker](https://docs.docker.com/get-docker/) installed if running the Durable Task Scheduler emulator locally
-- [Redis](https://redis.io/) (for sample 07 only) - can be run locally using Docker
-
-### Configuring RBAC Permissions for Azure OpenAI
-
-These samples are configured to use the Azure OpenAI service with RBAC permissions to access the model. You'll need to configure the RBAC permissions for the Azure OpenAI service to allow the console app to access the model.
-
-Below is an example of how to configure the RBAC permissions for the Azure OpenAI service to allow the current user to access the model.
-
-Bash (Linux/macOS/WSL):
-
-```bash
-az role assignment create \
-    --assignee "yourname@contoso.com" \
-    --role "Cognitive Services OpenAI User" \
-    --scope /subscriptions/<your-subscription-id>/resourceGroups/<your-resource-group-name>/providers/Microsoft.CognitiveServices/accounts/<your-openai-resource-name>
-```
-
-PowerShell:
-
-```powershell
-az role assignment create `
-    --assignee "yourname@contoso.com" `
-    --role "Cognitive Services OpenAI User" `
-    --scope /subscriptions/<your-subscription-id>/resourceGroups/<your-resource-group-name>/providers/Microsoft.CognitiveServices/accounts/<your-openai-resource-name>
-```
-
-More information on how to configure RBAC permissions for Azure OpenAI can be found in the [Azure OpenAI documentation](https://learn.microsoft.com/azure/ai-services/openai/how-to/create-resource?pivots=cli).
-
-### Setting an API key for the Azure OpenAI service
-
-As an alternative to configuring Azure RBAC permissions, you can set an API key for the Azure OpenAI service by setting the `AZURE_OPENAI_KEY` environment variable.
-
-Bash (Linux/macOS/WSL):
-
-```bash
-export AZURE_OPENAI_KEY="your-api-key"
-```
-
-PowerShell:
-
-```powershell
-$env:AZURE_OPENAI_KEY="your-api-key"
-```
-
-### Start Durable Task Scheduler
-
-Most samples use the Durable Task Scheduler (DTS) to support hosted agents and durable orchestrations. DTS also allows you to view the status of orchestrations and their inputs and outputs from a web UI.
-
-To run the Durable Task Scheduler locally, you can use the following `docker` command:
-
-```bash
-docker run -d --name dts-emulator -p 8080:8080 -p 8082:8082 mcr.microsoft.com/dts/dts-emulator:latest
-```
-
-The DTS dashboard will be available at `http://localhost:8080`.
-
-### Environment Configuration
-
-Each sample reads configuration from environment variables. You'll need to set the following environment variables:
-
-```bash
-export AZURE_OPENAI_ENDPOINT="https://your-resource.openai.azure.com/"
-export AZURE_OPENAI_DEPLOYMENT="your-deployment-name"
-```
-
-### Running the Console Apps
-
-Navigate to the sample directory and run the console app:
-
-```bash
-cd dotnet/samples/DurableAgents/ConsoleApps/01_SingleAgent
-dotnet run --framework net10.0
-```
-
-> [!NOTE]
-> The `--framework` option is required to specify the target framework for the console app because the samples are designed to support multiple target frameworks. If you are using a different target framework, you can specify it with the `--framework` option.
-
-The app will prompt you for input via stdin.
-
-### Viewing the sample output
-
-The console app output is displayed directly in the terminal where you ran `dotnet run`. Agent responses are printed to stdout with subtle color coding for better readability.
-
-You can also see the state of agents and orchestrations in the Durable Task Scheduler dashboard at `http://localhost:8082`.

dotnet/samples/DurableAgents/Directory.Build.props

@@ -1,9 +0,0 @@
-<Project>
-
-  <Import Project="../Directory.Build.props" />
-
-  <!-- Remove the Environment alias from parent Directory.Build.props to allow System.Environment usage -->
-  <ItemGroup>
-    <Using Remove="SampleHelpers.SampleEnvironment" />
-  </ItemGroup>
-</Project>

dotnet/samples/GettingStarted/A2A/A2AAgent_AsFunctionTools/Program.cs

@@ -23,14 +23,14 @@ A2ACardResolver agentCardResolver = new(new Uri(a2aAgentHost));
 AgentCard agentCard = await agentCardResolver.GetAgentCardAsync();
 
 // Create an instance of the AIAgent for an existing A2A agent specified by the agent card.
-AIAgent a2aAgent = agentCard.AsAIAgent();
+AIAgent a2aAgent = agentCard.GetAIAgent();
 
 // Create the main agent, and provide the a2a agent skills as a function tools.
 AIAgent agent = new AzureOpenAIClient(
     new Uri(endpoint),
     new AzureCliCredential())
     .GetChatClient(deploymentName)
-    .AsAIAgent(
+    .CreateAIAgent(
         instructions: "You are a helpful assistant that helps people with travel planning.",
         tools: [.. CreateFunctionTools(a2aAgent, agentCard)]
     );

dotnet/samples/GettingStarted/A2A/A2AAgent_PollingForTaskCompletion/Program.cs

@@ -14,12 +14,12 @@ A2ACardResolver agentCardResolver = new(new Uri(a2aAgentHost));
 AgentCard agentCard = await agentCardResolver.GetAgentCardAsync();
 
 // Create an instance of the AIAgent for an existing A2A agent specified by the agent card.
-AIAgent agent = agentCard.AsAIAgent();
+AIAgent agent = agentCard.GetAIAgent();
 
-AgentThread thread = await agent.GetNewThreadAsync();
+AgentThread thread = agent.GetNewThread();
 
 // Start the initial run with a long-running task.
-AgentResponse response = await agent.RunAsync("Conduct a comprehensive analysis of quantum computing applications in cryptography, including recent breakthroughs, implementation challenges, and future roadmap. Please include diagrams and visual representations to illustrate complex concepts.", thread);
+AgentRunResponse response = await agent.RunAsync("Conduct a comprehensive analysis of quantum computing applications in cryptography, including recent breakthroughs, implementation challenges, and future roadmap. Please include diagrams and visual representations to illustrate complex concepts.", thread);
 
 // Poll until the response is complete.
 while (response.ContinuationToken is { } token)

dotnet/samples/GettingStarted/AGUI/README.md

@@ -212,7 +212,7 @@ dotnet run
 
 1. `AGUIAgent` sends HTTP POST request to server
 2. Server responds with SSE stream
-3. Client parses events into `AgentResponseUpdate` objects
+3. Client parses events into `AgentRunResponseUpdate` objects
 4. Updates are displayed based on content type
 5. `ConversationId` maintains conversation context
 

dotnet/samples/GettingStarted/AGUI/Step01_GettingStarted/Client/Program.cs

@@ -16,11 +16,11 @@ using HttpClient httpClient = new()
 
 AGUIChatClient chatClient = new(httpClient, serverUrl);
 
-AIAgent agent = chatClient.AsAIAgent(
+AIAgent agent = chatClient.CreateAIAgent(
     name: "agui-client",
     description: "AG-UI Client Agent");
 
-AgentThread thread = await agent.GetNewThreadAsync();
+AgentThread thread = agent.GetNewThread();
 List<ChatMessage> messages =
 [
     new(ChatRole.System, "You are a helpful assistant.")
@@ -51,7 +51,7 @@ try
         bool isFirstUpdate = true;
         string? threadId = null;
 
-        await foreach (AgentResponseUpdate update in agent.RunStreamingAsync(messages, thread))
+        await foreach (AgentRunResponseUpdate update in agent.RunStreamingAsync(messages, thread))
         {
             ChatResponseUpdate chatUpdate = update.AsChatResponseUpdate();
 

dotnet/samples/GettingStarted/AGUI/Step01_GettingStarted/Server/Program.cs

@@ -24,7 +24,7 @@ ChatClient chatClient = new AzureOpenAIClient(
         new DefaultAzureCredential())
     .GetChatClient(deploymentName);
 
-AIAgent agent = chatClient.AsIChatClient().AsAIAgent(
+AIAgent agent = chatClient.AsIChatClient().CreateAIAgent(
     name: "AGUIAssistant",
     instructions: "You are a helpful assistant.");
 

dotnet/samples/GettingStarted/AGUI/Step02_BackendTools/Client/Program.cs

@@ -16,11 +16,11 @@ using HttpClient httpClient = new()
 
 AGUIChatClient chatClient = new(httpClient, serverUrl);
 
-AIAgent agent = chatClient.AsAIAgent(
+AIAgent agent = chatClient.CreateAIAgent(
     name: "agui-client",
     description: "AG-UI Client Agent");
 
-AgentThread thread = await agent.GetNewThreadAsync();
+AgentThread thread = agent.GetNewThread();
 List<ChatMessage> messages =
 [
     new(ChatRole.System, "You are a helpful assistant.")
@@ -51,7 +51,7 @@ try
         bool isFirstUpdate = true;
         string? threadId = null;
 
-        await foreach (AgentResponseUpdate update in agent.RunStreamingAsync(messages, thread))
+        await foreach (AgentRunResponseUpdate update in agent.RunStreamingAsync(messages, thread))
         {
             ChatResponseUpdate chatUpdate = update.AsChatResponseUpdate();
 

dotnet/samples/GettingStarted/AGUI/Step02_BackendTools/Server/Program.cs

@@ -79,7 +79,7 @@ ChatClient chatClient = new AzureOpenAIClient(
         new DefaultAzureCredential())
     .GetChatClient(deploymentName);
 
-ChatClientAgent agent = chatClient.AsIChatClient().AsAIAgent(
+ChatClientAgent agent = chatClient.AsIChatClient().CreateAIAgent(
     name: "AGUIAssistant",
     instructions: "You are a helpful assistant with access to restaurant information.",
     tools: tools);

dotnet/samples/GettingStarted/AGUI/Step03_FrontendTools/Client/Program.cs

@@ -28,12 +28,12 @@ using HttpClient httpClient = new()
 
 AGUIChatClient chatClient = new(httpClient, serverUrl);
 
-AIAgent agent = chatClient.AsAIAgent(
+AIAgent agent = chatClient.CreateAIAgent(
     name: "agui-client",
     description: "AG-UI Client Agent",
     tools: frontendTools);
 
-AgentThread thread = await agent.GetNewThreadAsync();
+AgentThread thread = agent.GetNewThread();
 List<ChatMessage> messages =
 [
     new(ChatRole.System, "You are a helpful assistant.")
@@ -64,7 +64,7 @@ try
         bool isFirstUpdate = true;
         string? threadId = null;
 
-        await foreach (AgentResponseUpdate update in agent.RunStreamingAsync(messages, thread))
+        await foreach (AgentRunResponseUpdate update in agent.RunStreamingAsync(messages, thread))
         {
             ChatResponseUpdate chatUpdate = update.AsChatResponseUpdate();
 

dotnet/samples/GettingStarted/AGUI/Step03_FrontendTools/Server/Program.cs

@@ -24,7 +24,7 @@ ChatClient chatClient = new AzureOpenAIClient(
         new DefaultAzureCredential())
     .GetChatClient(deploymentName);
 
-AIAgent agent = chatClient.AsIChatClient().AsAIAgent(
+AIAgent agent = chatClient.AsIChatClient().CreateAIAgent(
     name: "AGUIAssistant",
     instructions: "You are a helpful assistant.");
 

dotnet/samples/GettingStarted/AGUI/Step04_HumanInLoop/Client/Program.cs

@@ -16,7 +16,7 @@ using HttpClient httpClient = new()
 AGUIChatClient chatClient = new(httpClient, serverUrl);
 
 // Create agent
-ChatClientAgent baseAgent = chatClient.AsAIAgent(
+ChatClientAgent baseAgent = chatClient.CreateAIAgent(
     name: "AGUIAssistant",
     instructions: "You are a helpful assistant.");
 
@@ -51,8 +51,8 @@ while ((input = Console.ReadLine()) != null && !input.Equals("exit", StringCompa
     {
         approvalResponses.Clear();
 
-        List<AgentResponseUpdate> chatResponseUpdates = [];
-        await foreach (AgentResponseUpdate update in agent.RunStreamingAsync(messages, thread, cancellationToken: default))
+        List<AgentRunResponseUpdate> chatResponseUpdates = [];
+        await foreach (AgentRunResponseUpdate update in agent.RunStreamingAsync(messages, thread, cancellationToken: default))
         {
             chatResponseUpdates.Add(update);
             foreach (AIContent content in update.Contents)
@@ -111,7 +111,7 @@ while ((input = Console.ReadLine()) != null && !input.Equals("exit", StringCompa
             }
         }
 
-        AgentResponse response = chatResponseUpdates.ToAgentResponse();
+        AgentRunResponse response = chatResponseUpdates.ToAgentRunResponse();
         messages.AddRange(response.Messages);
         foreach (AIContent approvalResponse in approvalResponses)
         {

dotnet/samples/GettingStarted/AGUI/Step04_HumanInLoop/Client/ServerFunctionApprovalClientAgent.cs

@@ -22,17 +22,17 @@ internal sealed class ServerFunctionApprovalClientAgent : DelegatingAIAgent
         this._jsonSerializerOptions = jsonSerializerOptions;
     }
 
-    protected override Task<AgentResponse> RunCoreAsync(
+    protected override Task<AgentRunResponse> RunCoreAsync(
         IEnumerable<ChatMessage> messages,
         AgentThread? thread = null,
         AgentRunOptions? options = null,
         CancellationToken cancellationToken = default)
     {
         return this.RunCoreStreamingAsync(messages, thread, options, cancellationToken)
-            .ToAgentResponseAsync(cancellationToken);
+            .ToAgentRunResponseAsync(cancellationToken);
     }
 
-    protected override async IAsyncEnumerable<AgentResponseUpdate> RunCoreStreamingAsync(
+    protected override async IAsyncEnumerable<AgentRunResponseUpdate> RunCoreStreamingAsync(
         IEnumerable<ChatMessage> messages,
         AgentThread? thread = null,
         AgentRunOptions? options = null,
@@ -166,8 +166,8 @@ internal sealed class ServerFunctionApprovalClientAgent : DelegatingAIAgent
         return result ?? messages;
     }
 
-    private static AgentResponseUpdate ProcessIncomingServerApprovalRequests(
-        AgentResponseUpdate update,
+    private static AgentRunResponseUpdate ProcessIncomingServerApprovalRequests(
+        AgentRunResponseUpdate update,
         JsonSerializerOptions jsonSerializerOptions)
     {
         IList<AIContent>? updatedContents = null;
@@ -215,7 +215,7 @@ internal sealed class ServerFunctionApprovalClientAgent : DelegatingAIAgent
         if (updatedContents is not null)
         {
             var chatUpdate = update.AsChatResponseUpdate();
-            return new AgentResponseUpdate(new ChatResponseUpdate()
+            return new AgentRunResponseUpdate(new ChatResponseUpdate()
             {
                 Role = chatUpdate.Role,
                 Contents = updatedContents,

dotnet/samples/GettingStarted/AGUI/Step04_HumanInLoop/Server/Program.cs

@@ -57,7 +57,7 @@ ChatClient openAIChatClient = new AzureOpenAIClient(
         new DefaultAzureCredential())
     .GetChatClient(deploymentName);
 
-ChatClientAgent baseAgent = openAIChatClient.AsIChatClient().AsAIAgent(
+ChatClientAgent baseAgent = openAIChatClient.AsIChatClient().CreateAIAgent(
     name: "AGUIAssistant",
     instructions: "You are a helpful assistant in charge of approving expenses",
     tools: tools);

dotnet/samples/GettingStarted/AGUI/Step04_HumanInLoop/Server/ServerFunctionApprovalServerAgent.cs

@@ -22,17 +22,17 @@ internal sealed class ServerFunctionApprovalAgent : DelegatingAIAgent
         this._jsonSerializerOptions = jsonSerializerOptions;
     }
 
-    protected override Task<AgentResponse> RunCoreAsync(
+    protected override Task<AgentRunResponse> RunCoreAsync(
         IEnumerable<ChatMessage> messages,
         AgentThread? thread = null,
         AgentRunOptions? options = null,
         CancellationToken cancellationToken = default)
     {
         return this.RunCoreStreamingAsync(messages, thread, options, cancellationToken)
-            .ToAgentResponseAsync(cancellationToken);
+            .ToAgentRunResponseAsync(cancellationToken);
     }
 
-    protected override async IAsyncEnumerable<AgentResponseUpdate> RunCoreStreamingAsync(
+    protected override async IAsyncEnumerable<AgentRunResponseUpdate> RunCoreStreamingAsync(
         IEnumerable<ChatMessage> messages,
         AgentThread? thread = null,
         AgentRunOptions? options = null,
@@ -172,8 +172,8 @@ internal sealed class ServerFunctionApprovalAgent : DelegatingAIAgent
         return result ?? messages;
     }
 
-    private static AgentResponseUpdate ProcessOutgoingApprovalRequests(
-        AgentResponseUpdate update,
+    private static AgentRunResponseUpdate ProcessOutgoingApprovalRequests(
+        AgentRunResponseUpdate update,
         JsonSerializerOptions jsonSerializerOptions)
     {
         IList<AIContent>? updatedContents = null;
@@ -207,7 +207,7 @@ internal sealed class ServerFunctionApprovalAgent : DelegatingAIAgent
         {
             var chatUpdate = update.AsChatResponseUpdate();
             // Yield a tool call update that represents the approval request
-            return new AgentResponseUpdate(new ChatResponseUpdate()
+            return new AgentRunResponseUpdate(new ChatResponseUpdate()
             {
                 Role = chatUpdate.Role,
                 Contents = updatedContents,

dotnet/samples/GettingStarted/AGUI/Step05_StateManagement/Client/Program.cs

@@ -19,7 +19,7 @@ using HttpClient httpClient = new()
 
 AGUIChatClient chatClient = new(httpClient, serverUrl);
 
-AIAgent baseAgent = chatClient.AsAIAgent(
+AIAgent baseAgent = chatClient.CreateAIAgent(
     name: "recipe-client",
     description: "AG-UI Recipe Client Agent");
 
@@ -30,7 +30,7 @@ JsonSerializerOptions jsonOptions = new(JsonSerializerDefaults.Web)
 };
 StatefulAgent<AgentState> agent = new(baseAgent, jsonOptions, new AgentState());
 
-AgentThread thread = await agent.GetNewThreadAsync();
+AgentThread thread = agent.GetNewThread();
 List<ChatMessage> messages =
 [
     new(ChatRole.System, "You are a helpful recipe assistant.")
@@ -70,7 +70,7 @@ try
 
         Console.WriteLine();
 
-        await foreach (AgentResponseUpdate update in agent.RunStreamingAsync(messages, thread))
+        await foreach (AgentRunResponseUpdate update in agent.RunStreamingAsync(messages, thread))
         {
             ChatResponseUpdate chatUpdate = update.AsChatResponseUpdate();
 

dotnet/samples/GettingStarted/AGUI/Step05_StateManagement/Client/StatefulAgent.cs

@@ -35,18 +35,18 @@ internal sealed class StatefulAgent<TState> : DelegatingAIAgent
     }
 
     /// <inheritdoc />
-    protected override Task<AgentResponse> RunCoreAsync(
+    protected override Task<AgentRunResponse> RunCoreAsync(
         IEnumerable<ChatMessage> messages,
         AgentThread? thread = null,
         AgentRunOptions? options = null,
         CancellationToken cancellationToken = default)
     {
         return this.RunCoreStreamingAsync(messages, thread, options, cancellationToken)
-            .ToAgentResponseAsync(cancellationToken);
+            .ToAgentRunResponseAsync(cancellationToken);
     }
 
     /// <inheritdoc />
-    protected override async IAsyncEnumerable<AgentResponseUpdate> RunCoreStreamingAsync(
+    protected override async IAsyncEnumerable<AgentRunResponseUpdate> RunCoreStreamingAsync(
         IEnumerable<ChatMessage> messages,
         AgentThread? thread = null,
         AgentRunOptions? options = null,
@@ -64,7 +64,7 @@ internal sealed class StatefulAgent<TState> : DelegatingAIAgent
         messagesWithState.Add(stateMessage);
 
         // Stream the response and update state when received
-        await foreach (AgentResponseUpdate update in this.InnerAgent.RunStreamingAsync(messagesWithState, thread, options, cancellationToken))
+        await foreach (AgentRunResponseUpdate update in this.InnerAgent.RunStreamingAsync(messagesWithState, thread, options, cancellationToken))
         {
             // Check if this update contains a state snapshot
             foreach (AIContent content in update.Contents)

dotnet/samples/GettingStarted/AGUI/Step05_StateManagement/Server/Program.cs

@@ -34,7 +34,7 @@ ChatClient chatClient = new AzureOpenAIClient(
         new DefaultAzureCredential())
     .GetChatClient(deploymentName);
 
-AIAgent baseAgent = chatClient.AsIChatClient().AsAIAgent(
+AIAgent baseAgent = chatClient.AsIChatClient().CreateAIAgent(
     name: "RecipeAgent",
     instructions: """
         You are a helpful recipe assistant. When users ask you to create or suggest a recipe,